Research Index
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A specific company
| Company | Primary | Also mentioned in |
|---|---|---|
| Waymo | 80-industry-intel/companies/waymo/ (5 docs) | 30-autonomy-stack/end-to-end-driving/company-approaches.md, 60-safety-validation/safety-case/safety-incidents-lessons.md, 50-cloud-fleet/ota/ota-fleet-management.md, 50-cloud-fleet/fleet-management/fleet-management-dispatch.md, 30-autonomy-stack/perception/overview/production-perception-systems.md |
| Tesla | 80-industry-intel/companies/tesla/ (4 docs) | 30-autonomy-stack/end-to-end-driving/company-approaches.md, 60-safety-validation/safety-case/safety-incidents-lessons.md, 50-cloud-fleet/ota/ota-fleet-management.md, 30-autonomy-stack/perception/overview/production-perception-systems.md |
| comma.ai | 80-industry-intel/companies/comma-ai/ (2 docs) | 30-autonomy-stack/world-models/opensource-implementations.md, 60-safety-validation/verification-validation/shadow-mode.md, 40-runtime-systems/ml-deployment/opensource-ecosystem.md |
| UISEE | 80-industry-intel/companies/uisee/tech-stack.md | 80-industry-intel/companies/changi-programme/, 70-operations-domains/airside/operations/industry-overview.md |
| TractEasy/EasyMile | 80-industry-intel/companies/tracteasy/ (2 docs) | 60-safety-validation/standards-certification/iso-3691-4-deep-dive.md, 70-operations-domains/airside/operations/industry-overview.md |
| Wayve | 80-industry-intel/companies/wayve/ (4 docs) | 30-autonomy-stack/end-to-end-driving/company-approaches.md, 30-autonomy-stack/world-models/overview.md |
| AeroVect | 80-industry-intel/companies/aerovect/tech-stack.md | 70-operations-domains/airside/operations/industry-overview.md |
| Assaia | 80-industry-intel/companies/assaia/tech-stack.md | 80-industry-intel/companies/moonware/halo-operations.md |
| Fernride | 80-industry-intel/companies/fernride/tech-stack.md | 40-runtime-systems/monitoring-observability/teleoperation-systems.md |
| Applied Intuition | 80-industry-intel/companies/applied-intuition/tech-stack.md | 30-autonomy-stack/simulation/airport-digital-twins.md |
World models
| Topic | Primary | Supporting |
|---|---|---|
| What are world models | 30-autonomy-stack/world-models/overview.md | 90-synthesis/master/master-synthesis.md |
| World-model first principles | 10-knowledge-base/machine-learning/world-models-first-principles.md | Latent state, transition models, observation/reward heads, Dreamer/PlaNet, tokenized, diffusion, and JEPA branches |
| Diffusion-based | 30-autonomy-stack/world-models/diffusion-world-models.md | 10-knowledge-base/machine-learning/diffusion-models.md |
| Occupancy-based | 30-autonomy-stack/world-models/occupancy-world-models.md | 30-autonomy-stack/world-models/occupancy-networks-comparison.md (20 methods) |
| Tokenized / JEPA | 30-autonomy-stack/world-models/tokenized-and-jepa.md | 10-knowledge-base/machine-learning/vqvae-tokenization.md, 10-knowledge-base/machine-learning/jepa-latent-predictive-learning.md |
| RL with world models | 30-autonomy-stack/world-models/rl-with-world-models.md | 30-autonomy-stack/world-models/dreamer-world-model-rl.md |
| OccWorld setup | 30-autonomy-stack/world-models/occworld-implementation.md | 30-autonomy-stack/world-models/occupancy-networks-comparison.md |
| Open-source repos | 30-autonomy-stack/world-models/opensource-implementations.md | 21 repos rated |
| Cutting edge 2026 | 30-autonomy-stack/world-models/cutting-edge-2026.md | Latest papers and SOTA |
| Occupancy on Orin | 30-autonomy-stack/world-models/occupancy-deployment-orin.md | FlashOcc TensorRT, nvblox, LiDAR voxelization, multi-resolution grids |
| LiDAR-native world models | 30-autonomy-stack/world-models/lidar-native-world-models.md | Copilot4D, UnO, LidarDM, LiDARCrafter, 4D occupancy forecasting, point cloud prediction, AD-L-JEPA, self-supervised training, Orin deployment |
| Occupancy flow & 4D scenes | 30-autonomy-stack/world-models/occupancy-flow-4d-scenes.md | Scene flow (ZeroFlow 0.028m EPE, DeFlow SOTA), 4D occupancy forecasting (UnO, OccSora, Cam4DOcc), dynamic 3D Gaussians, K-Planes 10900x compression, flow-guided Frenet planning, Mamba temporal, Orin 26-40ms FP16, $6-11K training |
| Self-supervised occupancy flow | 30-autonomy-stack/world-models/self-supervised-occupancy-flow.md | Let Occ Flow, SelfOccFlow, static/dynamic field decomposition, self-supervised 3D occupancy-flow training, and label-cost reduction for dynamic scenes |
| Occupancy-centric scene generation | 30-autonomy-stack/world-models/uniscene-occupancy-centric-generation.md | UniScene-style semantic occupancy as the shared representation for generated video, LiDAR, and inspectable synthetic-data supervision |
| Scene flow for removal | 30-autonomy-stack/world-models/scene-flow-for-dynamic-object-removal.md | Connects LiDAR scene flow, MOS, occupancy flow, static-map cleaning, and planner-facing dynamic-object evidence |
| Scene-flow benchmarks | 30-autonomy-stack/world-models/scene-flow-datasets-benchmarks.md | FlyingThings3D, KITTI Scene Flow, Argoverse 2 flow, Waymo flow, ZeroFlow/DeFlow evaluation, and removal-oriented metrics |
Machine learning foundations
| Topic | Primary | Supporting |
|---|---|---|
| ML foundation ladder | 10-knowledge-base/machine-learning/overview.md | Reading path from perceptron and logits through backprop, optimization, CNNs, RNNs, transformers, Mamba, JEPA, and world models |
| Linear and probabilistic classifiers | 10-knowledge-base/machine-learning/perceptron-linear-classifiers.md | 10-knowledge-base/machine-learning/logistic-softmax-cross-entropy.md |
| Training mechanics | 10-knowledge-base/machine-learning/backprop-computational-graphs-autodiff.md | 10-knowledge-base/machine-learning/optimization-training-dynamics.md, 10-knowledge-base/machine-learning/initialization-normalization-regularization.md |
| Spatial and temporal neural networks | 10-knowledge-base/machine-learning/convolutional-neural-networks.md | 10-knowledge-base/machine-learning/recurrent-neural-networks-lstm-gru.md, 10-knowledge-base/machine-learning/sequence-models-rnn-ssm-attention-first-principles.md |
| Transformer and foundation models | 10-knowledge-base/machine-learning/attention-transformers-first-principles.md | 10-knowledge-base/machine-learning/vision-transformers-first-principles.md, 10-knowledge-base/machine-learning/foundation-model-training-first-principles.md |
| Self-supervised and predictive learning | 10-knowledge-base/machine-learning/self-supervised-learning-first-principles.md | 10-knowledge-base/machine-learning/jepa-latent-predictive-learning.md, 10-knowledge-base/machine-learning/world-models-first-principles.md |
| Representation objectives | 10-knowledge-base/machine-learning/contrastive-learning-infonsce-first-principles.md | 10-knowledge-base/machine-learning/masked-modeling-first-principles.md, 10-knowledge-base/machine-learning/energy-based-models-first-principles.md, 10-knowledge-base/machine-learning/autoencoders-vae-and-latent-variable-models-first-principles.md |
| Sequence, tokens, and generators | 10-knowledge-base/machine-learning/state-space-models-s4-mamba-first-principles.md | 10-knowledge-base/machine-learning/tokenization-and-discretization-first-principles.md, 10-knowledge-base/machine-learning/positional-encodings-and-coordinate-tokenization-first-principles.md, 10-knowledge-base/machine-learning/diffusion-score-flow-samplers-first-principles.md |
| Evaluation and objective design | 10-knowledge-base/machine-learning/evaluation-calibration-and-data-leakage-first-principles.md | 10-knowledge-base/machine-learning/multi-task-losses-and-objectives-first-principles.md, 10-knowledge-base/machine-learning/world-model-evaluation-and-planning-objectives-first-principles.md |
Perception
| Topic | Primary | Supporting |
|---|---|---|
| BEV encoding | 30-autonomy-stack/perception/overview/bev-encoding.md | 10-knowledge-base/geometry-3d/pointpillars.md |
| Open-vocab detection | 30-autonomy-stack/perception/overview/open-vocab-detection.md | YOLO-World, Grounding DINO |
| DINOv2 for driving | 30-autonomy-stack/perception/overview/dinov2-foundation-models-driving.md | LoRA, adapter integration |
| CenterPoint/OpenPCDet | 30-autonomy-stack/perception/overview/openpcdet-centerpoint.md | 20-av-platform/compute/tensorrt-deployment-guide.md |
| Production systems | 30-autonomy-stack/perception/overview/production-perception-systems.md | Waymo/Tesla/comma sensor suites |
| Perception method library | 30-autonomy-stack/perception/methods/overview.md | 93 atomic method files across camera BEV, occupancy/free-space, LiDAR-camera/radar-camera fusion, dynamic Gaussian/3DGS/4DGS, LiDAR MOS, scene flow, LiDAR denoising/removal, radar/4D radar, event/FMCW, open-world/OOD, open-vocabulary attributes, robust fusion, V2X, latency, and data-engine evaluation |
| LiDAR artifact removal | 30-autonomy-stack/perception/overview/lidar-artifact-removal-techniques.md | LIORNet, LiSnowNet, SLiDE, TripleMixer, classical filters, weather artifacts, ghost/multipath behavior, dynamic-map cleaning, and validation |
| Weather robustness datasets | 30-autonomy-stack/perception/datasets-benchmarks/weather-robustness-datasets.md | WADS, CADC/CADC+, SemanticSTF, REHEARSE-3D, RainSense, SemanticSpray, RADIATE, and Seeing Through Fog/DENSE |
| Moving/static separation datasets | 30-autonomy-stack/perception/datasets-benchmarks/moving-static-separation-mos-datasets.md | SemanticKITTI-MOS, HeLiMOS, 4DMOS-style labels, moving/static taxonomy, and map-cleaning evaluation fit |
| Occupancy-flow benchmarks | 30-autonomy-stack/perception/datasets-benchmarks/occupancy-flow-and-4d-occupancy-benchmarks.md | Cam4DOcc, OpenOccupancy, Occ3D/OpenScene, UniOcc, nuCraft, and 4D occupancy metrics for flow/removal systems |
| Adverse/OOD/FOD/V2X benchmarks | 30-autonomy-stack/perception/datasets-benchmarks/muses-multisensor-adverse-semantic-perception.md, 30-autonomy-stack/perception/datasets-benchmarks/sensor-corruption-robustness-benchmarks.md, 30-autonomy-stack/perception/datasets-benchmarks/open-world-ood-anomaly-segmentation-benchmarks.md, 30-autonomy-stack/perception/datasets-benchmarks/stu-3d-lidar-anomaly-segmentation.md, 30-autonomy-stack/perception/datasets-benchmarks/fod-and-airport-apron-detection-datasets.md, 30-autonomy-stack/perception/datasets-benchmarks/airside-fod-synthetic-multimodal-benchmarks.md, 30-autonomy-stack/perception/datasets-benchmarks/rcp-bench-cooperative-corruption-robustness.md, 30-autonomy-stack/perception/datasets-benchmarks/v2x-large-range-sequential-datasets.md | MUSES, Robo3D/MultiCorrupt-style corruption tests, STU 3D anomaly segmentation, SegmentMeIfYouCan/OpenAD-style anomaly segmentation, airport FOD and synthetic multimodal FOD benchmark framing, cooperative corruption robustness, and large-range V2X datasets |
| Perception coverage audit | 30-autonomy-stack/perception/overview/coverage-audit-2026.md | May 2026 multi-agent sweeps across camera BEV/occupancy, LiDAR MOS, 4D radar, open-world/OOD, V2X, robust fusion, deployment validation, and benchmarks |
| Sensor fusion | 30-autonomy-stack/perception/overview/sensor-fusion-architectures.md | BEVFusion, masked modality training |
| Infrastructure cooperative perception | 30-autonomy-stack/perception/overview/infrastructure-cooperative-perception.md | V2I fusion, fixed sensors, DAIR-V2X, airport existing systems |
| LiDAR foundation models | 30-autonomy-stack/perception/overview/lidar-foundation-models.md | PTv3, Sonata, ScaLR, PointLoRA, 50-80% data savings |
| LiDAR semantic segmentation | 30-autonomy-stack/perception/overview/lidar-semantic-segmentation.md | Cylinder3D, FlatFormer, PTv3, ALPINE panoptic, airside 18-class taxonomy |
| Model compression & edge | 30-autonomy-stack/perception/overview/model-compression-edge-deployment.md | PTQ/QAT, distillation, pruning, TensorRT, ModelOpt, Orin recipes |
| Multi-object tracking | 30-autonomy-stack/perception/overview/multi-object-tracking.md | CenterPoint tracker, SimpleTrack, MCTrack, HOTA, airside Re-ID |
| Camera fallback perception | 30-autonomy-stack/perception/overview/camera-fallback-perception.md | Degraded mode when LiDAR fails: DepthAnything v2, stereo depth, BEVFormer-Tiny, confidence calibration, speed reduction |
| Collaborative fleet perception | 30-autonomy-stack/perception/overview/collaborative-fleet-perception.md | V2V cooperative sensing, Where2comm bandwidth selection, CoBEVT/CoBEVFlow temporal fusion, HEAL heterogeneous agents, fleet occupancy map, collective FOD detection, 5G deployment |
| V2X protocols & airside messages | 30-autonomy-stack/multi-agent-v2x/v2x-protocols-airside.md | C-V2X vs DSRC (5G NR V2X preferred), ETSI ITS (CAM/DENM/CPM/MCM), 8 airside-specific messages (APA, SOS, GTA, DZN, EVP, RIP, FDA, JBW), protobuf specs, A-CDM/A-SMGCS/ADS-B bridge, PKI security, bandwidth planning (123 Mbps/50 vehicles), default-deny runway clearance, $270-450K full capability |
| Fleet task allocation & scheduling | 30-autonomy-stack/multi-agent-v2x/fleet-task-allocation-scheduling.md | MRTA MT-SR-TA formulation, MILP/CP-SAT (OR-Tools optimal in 10-60s for 200 vehicles), Hungarian O(n³) single-assignment, CBBA decentralized auction (95% optimal, <100ms), SSI real-time auction, A-CDM predictive scheduling (ELDT→pre-positioning, 60-75% delay reduction), online reactive scheduling (event-driven rescheduling, 85% stability), RL dispatch policy (<1ms inference), charging-aware scheduling, multi-objective (tardiness+energy+safety), priority-based task shedding, $42-67K/15-17 weeks |
| Ramp traffic conflict & deadlock prevention | 30-autonomy-stack/multi-agent-v2x/ramp-traffic-conflict-deadlock-prevention.md | Zone-capacity graph from Lanelet2, reservation-based traffic management, wait-die deadlock prevention (guarantees no circular wait), 9-level priority conflict resolution, stand turnaround sequencing, V2X decentralized fallback, token mutex for single-lane zones, MAPF (CBS/ECBS for offline, PIBT for real-time), livelock detection/resolution, capacity-constrained routing, dispatch-traffic integration, $50-75K/17 weeks |
| Self-supervised pre-training | 30-autonomy-stack/perception/overview/self-supervised-pretraining-driving.md | Contrastive (SLidR, ScaLR), MAE (Voxel-MAE, GD-MAE, BEV-MAE), JEPA (AD-L-JEPA, V-JEPA 2), DINOv2, multi-modal pre-training, LoRA fine-tuning, 50-80% label reduction, airside curriculum strategy |
| 3DGS for perception & mapping | 30-autonomy-stack/perception/overview/gaussian-splatting-driving.md | GaussianFormer/GaussianOcc, SplatAD, streaming Gaussian occupancy, SplaTAM/MonoGS/Splat-SLAM/S3PO-GS, LiDAR-Gaussian fusion, dynamic object tracking, semantic Gaussians, FOD detection, aircraft proximity, Orin deployment notes |
| Dynamic Gaussian/neural-field perception | 30-autonomy-stack/perception/methods/drivinggaussian.md, 30-autonomy-stack/perception/methods/hugs-urban-gaussians.md, 30-autonomy-stack/perception/methods/splatflow.md, 30-autonomy-stack/perception/methods/distillnerf.md | Dynamic 3DGS/4DGS, holistic urban Gaussians, self-supervised Gaussian motion flow, and NeRF-to-occupancy distillation for perception and simulation reuse |
| Photoreal city-scale 4D reconstruction | 30-autonomy-stack/localization-mapping/overview/photoreal-city-scale-4d-reconstruction.md, 10-knowledge-base/geometry-3d/feed-forward-3d-reconstruction-and-splatting.md, 10-knowledge-base/mapping/dynamic-4d-neural-gaussian-reconstruction.md | Cross-section hub and first-principles pages for Gaussian-LIC/LIC2, VGGT, AnySplat, pixelSplat, Street Gaussians, OmniRe, S3Gaussian, EmerNeRF, OG-Gaussian, PVG, and DrivingGaussian |
| 4D radar-camera, radar-LiDAR, and FMCW perception | 30-autonomy-stack/perception/methods/cvfusion.md, 30-autonomy-stack/perception/methods/4d-radar-camera-occupancy.md, 30-autonomy-stack/perception/methods/adverse-weather-radar-lidar-3d-detection.md, 30-autonomy-stack/perception/methods/robucdet.md, 30-autonomy-stack/perception/methods/samfusion.md, 30-autonomy-stack/perception/methods/pod-fmcw-lidar-predictive-detection.md | Cross-view radar-camera detection, radar-camera semantic occupancy, radar-LiDAR adverse-weather detection, robust radar-camera BEV, sensor-adaptive multimodal fusion, and FMCW LiDAR velocity-aware predictive detection |
| Occupancy fusion and open-world occupancy | 30-autonomy-stack/perception/methods/lidar-camera-occupancy-fusion.md, 30-autonomy-stack/perception/methods/dynamic-occupancy-freespace.md, 30-autonomy-stack/perception/methods/spatiotemporal-memory-occupancy-flow.md, 30-autonomy-stack/perception/methods/open-vocabulary-panoptic-occupancy.md, 30-autonomy-stack/perception/methods/ovad-ovoda-open-vocab-3d-attributes.md | LiDAR-camera semantic occupancy fusion, dynamic/free-space occupancy, temporal occupancy memory, language/panoptic occupancy, and open-vocabulary 3D attributes for state-rich object semantics |
| Uncertainty quantification | 30-autonomy-stack/perception/overview/uncertainty-quantification-calibration.md | Epistemic/aleatoric decomposition, MC-Dropout (T=3, 21.5ms), deep ensembles (M=5, 0.93 AUROC), evidential deep learning (single pass, 7.5ms), conformal prediction (99% coverage guarantee), temperature scaling (ECE 0.03), LiDAR range-dependent uncertainty, multi-LiDAR fusion (65% reduction), teleop trigger criteria |
| Multi-task unified perception | 30-autonomy-stack/perception/overview/multi-task-unified-perception.md | UniAD (CVPR 2023 Best Paper), SparseDrive (3x faster), VAD-Tiny (80ms Orin), StreamPETR, shared-backbone multi-head (14.8ms on Orin, 56% savings), task interference/PCGrad, uncertainty-weighted loss, incremental deployment, 14-class airside segmentation |
| Night operations & thermal fusion | 30-autonomy-stack/perception/overview/night-operations-thermal-fusion.md | LiDAR-primary + thermal-augmented architecture, YOLO-Thermal INT8 (6-8ms Orin), asymmetric late fusion (+8-10ms), hi-vis paradox solved (84-88% camera AEB failure → 85-92% thermal AP), heated-target calibration (<0.5deg), jet blast/fuel spill thermal detection, night ODD (subset of daytime), DINOv2 LoRA thermal adapter, 22.8-25.8ms total pipeline (38-44 Hz), $6,700-22,600/vehicle |
| Streaming temporal perception | 30-autonomy-stack/perception/overview/streaming-temporal-perception.md | StreamPETR (+6-8% NDS, <3ms overhead, implicit tracking), Sparse4D v3 (71.9% NDS SOTA), multi-sweep LiDAR accumulation (3-sweep: +2.5% mAP, +1.4ms), latency compensation (ASAP/LASP), temporal filtering eliminates transient noise (de-icing spray, jet blast shimmer), extended airside track persistence (10-30s for GSE occlusion), video backbones vs query propagation, turnaround phase detection, $38K/13 weeks |
| Active perception & sensor scheduling | 30-autonomy-stack/perception/overview/active-perception-sensor-scheduling.md | Context-aware model switching (35-45% compute reduction), information-theoretic sensor selection (entropy-based attention), foveated LiDAR (89% voxel reduction), multi-LiDAR scheduling (3-4 of 8 LiDARs full at any time), early exit networks (48% average compute), risk-aware allocation (safety-critical always first), planner-guided attention, 30-36% power savings for electric GSE, $25-40K/10 weeks |
Method-level SLAM
| Topic | Primary | Supporting |
|---|---|---|
| SLAM method library | 30-autonomy-stack/localization-mapping/slam-methods/overview.md | 100 focused method files plus overview/audit pages covering classical, LiDAR, LIVO, visual, RGB-D, neural, Gaussian, radar, fusion SLAM, robust backends, collaborative SLAM, alternative sensors, lifelong localization, and map cleaning |
| SLAM coverage audit | 30-autonomy-stack/localization-mapping/slam-methods/coverage-audit-2026.md | Source-backed backlog plus May 2026 discovery sweeps: LVI-SAM, FAST-LIVO/R3LIVE, KISS-SLAM, MOLA, robust/certifiable PGO, C-SLAM systems, degeneracy-robust LIO, event/thermal/UWB VIO and localization, radar-to-LiDAR map matching, 4D radar, Gaussian/foundation SLAM, and current benchmarks |
| AV / indoor / outdoor selection | 30-autonomy-stack/localization-mapping/slam-methods/av-indoor-outdoor-decision-matrix.md | Method fit by GNSS availability, dynamics, map dependence, compute budget, and safety criticality |
| Benchmarks and datasets | 30-autonomy-stack/localization-mapping/slam-methods/benchmarking-metrics-datasets.md | ATE/RPE, KITTI drift, loop closure, map quality, dynamic-scene metrics, KITTI/KITTI-360, EuRoC, TUM, Oxford, Boreas, MulRan |
| Open-source stacks | 30-autonomy-stack/localization-mapping/slam-methods/open-source-stack-comparison.md | ORB-SLAM3, RTAB-Map, Cartographer, OpenVINS, Kimera, KISS-ICP, LIO-SAM, FAST-LIO2, GLIM, GTSAM, Open3D |
| Robust and collaborative SLAM backends | 30-autonomy-stack/localization-mapping/slam-methods/robust-pgo-gnc-risam.md, 30-autonomy-stack/localization-mapping/slam-methods/certifiable-pose-graph-optimization.md, 30-autonomy-stack/localization-mapping/slam-methods/kimera-rpgo-pcm.md, 30-autonomy-stack/localization-mapping/slam-methods/distributed-multi-robot-pgo.md, 30-autonomy-stack/localization-mapping/slam-methods/kimera-multi.md, 30-autonomy-stack/localization-mapping/slam-methods/covins-covins-g.md, 30-autonomy-stack/localization-mapping/slam-methods/d2slam.md | GNC/Black-Rangarajan/riSAM, SE-Sync/Shonan-style certifiable PGO, pairwise consistency loop verification, distributed PGO, and full collaborative SLAM systems |
| Classical SLAM foundations | 30-autonomy-stack/localization-mapping/slam-methods/graphslam-pose-graph-optimization.md | ekf-slam.md, fastslam-particle-slam.md, bundle-adjustment-slam.md, factor-graph-isam2-gtsam.md, lidar-bundle-adjustment-factors.md, scan-context-family.md, loop-closure-place-recognition.md, occupancy-grid-tsdf-esdf-mapping.md |
| Point-cloud registration | 30-autonomy-stack/localization-mapping/slam-methods/gicp-vgicp.md | icp.md, point-to-plane-icp.md, ndt.md, continuous-time-registration.md |
| 3D LiDAR SLAM | 30-autonomy-stack/localization-mapping/slam-methods/kiss-icp.md | loam.md, lego-loam.md, hdl-graph-slam.md, ct-icp.md, lio-sam.md, fast-lio-fast-lio2.md, point-lio.md, glim.md, cartographer-3d.md, suma.md |
| Visual and visual-inertial SLAM | 30-autonomy-stack/localization-mapping/slam-methods/orb-slam2-orb-slam3.md | lsd-slam-dso.md, svo.md, vins-mono-vins-fusion.md, openvins.md, okvis2-x.md, kimera-vio.md, event-camera-vio-slam.md, thermal-inertial-slam.md, droid-slam.md, dpvo.md, mast3r-slam.md |
| Indoor and dense SLAM | 30-autonomy-stack/localization-mapping/slam-methods/rtab-map.md | kinectfusion.md, elasticfusion.md, bundlefusion.md, imap.md, nice-slam.md, co-slam-eslam.md, nerf-slam.md |
| Learned, semantic, and Gaussian SLAM | 30-autonomy-stack/localization-mapping/slam-methods/splatam.md | lo-net-learned-lidar-odometry.md, regformer-learned-registration.md, semantic-slam.md, dynamic-object-aware-slam.md, object-level-slam.md, gs-slam-monogs.md, photo-slam.md |
| Outdoor Gaussian, radar, and degraded-sensor SLAM | 30-autonomy-stack/localization-mapping/slam-methods/splat-loam.md | gigaslam.md, wildgs-slam.md, splat-slam.md, s3po-gs.md, gaussian-lic.md, gs-livm.md, vigs-slam.md, dynamic-4d-gaussian-slam.md, radarsplat-rio.md, 4d-imaging-radar-rio-slam.md, radar-to-lidar-map-localization.md, radar-odometry-radar-slam.md, radar-inertial-odometry.md, radar-lidar-inertial-fusion.md, uwb-radio-ranging-slam.md, mm-lins.md |
| Dynamic map cleaning | 30-autonomy-stack/localization-mapping/slam-methods/lidar-map-cleaning-dynamic-removal.md | erasor.md, removert.md, mapcleaner.md, erasor-plus-plus.md, 4dndf.md, freedom-dynamic-object-removal.md, static-lio-dynamic-points-removal.md, moves-and-label-free-map-cleaning.md, benchmark coverage, static-map preservation, and dynamic-object removal risks |
| Lifelong and alternative localization | 30-autonomy-stack/localization-mapping/slam-methods/lt-mapper-khronos-lifelong-mapping.md, 30-autonomy-stack/localization-mapping/slam-methods/rtmap-dufomap-recursive-maintenance.md, 30-autonomy-stack/localization-mapping/slam-methods/gpr-localization-ground-encoding.md, 30-autonomy-stack/localization-mapping/slam-methods/radar-teach-repeat-localization.md | Long-term metric-semantic mapping, recursive map maintenance, ground-penetrating radar localization, and radar teach-and-repeat fallbacks for adverse weather or changed scenes |
Localization & mapping
| Topic | Primary | Supporting |
|---|---|---|
| Mapping & localization overview | 30-autonomy-stack/localization-mapping/overview/mapping-and-localization.md | MapTR, NMP, Tesla/Mobileye, SLAM |
| Map-free driving for airports | 30-autonomy-stack/localization-mapping/maps/map-free-driving.md | Three-layer map, AIXM prior, 10-25x faster deployment |
| HD map standards (airside) | 30-autonomy-stack/localization-mapping/maps/hd-map-standards-airside.md | OpenDRIVE, AMDB/AMXM, NDS, NOTAM integration, AIRAC cycle |
| Neural online mapping SOTA | 30-autonomy-stack/localization-mapping/maps/neural-online-mapping-sota.md | MapTracker, StreamMapNet, NMP, topology (TopoMLP, LaneSegNet) |
| LiDAR SLAM algorithms | 30-autonomy-stack/localization-mapping/overview/lidar-slam-algorithms.md | KISS-ICP, LIO-SAM, FAST-LIO2, Point-LIO, degeneracy handling |
| Semantic mapping & learned priors | 30-autonomy-stack/localization-mapping/maps/semantic-mapping-learned-priors.md | Neural Map Prior (NMP +5.4 mAP), PriorDrive, T2SG topology graphs, conformal map uncertainty, fleet-based incremental updates, 7-layer semantic map, multi-airport LoRA adapters |
| HD map change detection & maintenance | 30-autonomy-stack/localization-mapping/maps/hd-map-change-detection-maintenance.md | Point cloud differencing, semantic change detection, RTMap (ICCV 2025 centimeter-level), Bayesian fleet consensus, AIRAC integration, temporal decay models, light-map alternative (~720 KB), NMP implicit maintenance, OTA canary deployment, construction zone detection, cost 60-80% reduction vs manual re-survey, $45-70K/28 weeks |
| Moved-object and map-change datasets | 30-autonomy-stack/localization-mapping/maps/moved-object-and-map-change-datasets.md | RTMap/ExelMap-style change detection, 3RScan/Objects Can Move, TbV, POCD, FOD-A, dynamic-map benchmarks, and fleet-consensus validation |
| LiDAR place recognition & re-localization | 30-autonomy-stack/localization-mapping/overview/lidar-place-recognition-relocalization.md | Scan Context (<5ms CPU) + MinkLoc3D (97.5% recall@1, 15ms GPU) two-stage pipeline, PointNetVLAD, LoGG3D-Net, LCDNet (integrated pose), PPT few-shot, BEVPlace, FAISS million-scale retrieval (<1ms), GTSAM loop closure factors, kidnapped robot recovery, fleet shared descriptors, identical-stands disambiguation, seasonal databases, $33-57K/12-16 weeks |
| Robust state estimation & multi-sensor fusion | 30-autonomy-stack/localization-mapping/overview/robust-state-estimation-multi-sensor.md | ESKF (Error-State Kalman Filter) with quaternion error parameterization, chi-squared innovation gating, Mahalanobis sensor validation, multi-hypothesis tracking (IMM), GPS-denied dead-reckoning budgets, adaptive noise estimation (Sage-Husa), covariance management, fleet-level state consistency, <0.5ms per update on Orin, robot_localization integration |
| Real-time occupancy grid mapping | 30-autonomy-stack/localization-mapping/maps/realtime-occupancy-grid-mapping.md | Log-odds Bayesian update, OctoMap/VDBFusion/nvblox comparison, GPU raycasting (CUDA), multi-LiDAR fusion (4-8 sensors at 10Hz), dynamic object separation, multi-resolution grids (0.1-0.8m), TSDF/ESDF for planning, costmap generation for Frenet planner, fleet-shared occupancy over 5G, airside-specific (aircraft stands, jet blast), $25-40K |
| HD map construction pipeline | 30-autonomy-stack/localization-mapping/maps/map-construction-pipeline.md | End-to-end offline map building: survey drive planning (3 drive patterns), multi-session SLAM (FAST-LIO2+GTSAM), point cloud post-processing (dynamic object removal via multi-session voting), geodetic alignment (RTK+GCPs ±5-10cm global), AMDB overlay and co-registration, automated annotation (SAM+CLIP 85-92% accuracy), Lanelet2 generation, QA validation (20 automated checks), map packaging and OTA deployment, DVC version control, map CI/CD pipeline, 5-7 days per airport at $20-35K, scaling to $12-19K/airport at 20 airports |
| Production LiDAR-to-map localization | 30-autonomy-stack/localization-mapping/overview/production-lidar-map-localization.md | Runtime scan-to-map matching pipeline: ICP/GICP/VGICP/NDT algorithm comparison, multi-resolution coarse-to-fine (NDT→VGICP), eigenvalue-based degeneracy detection and handling, airside-specific challenges (40-70% dynamic content at stands, jet blast shimmer, ground reflectivity), multi-LiDAR fusion strategies (merge-then-match, match-then-fuse, selective), GTSAM factor graph integration with adaptive noise models, 5-level fallback hierarchy (VGICP→NDT→GPS→dead reckoning→safe stop), learned registration (GeoTransformer for cold start), Orin GPU deployment (15-25ms typical), $30-53K/12 weeks |
| Map tile versioning & distribution | 30-autonomy-stack/localization-mapping/maps/map-tile-versioning-distribution.md | Map lifecycle from build to vehicle: spatial tiling (50-200m tiles), content-addressable versioning (SHA-256 Merkle tree), differential updates (bsdiff, 2-8% of full tile), distribution over airport 5G (<30s/tile), NVMe vehicle-side storage, atomic map swap protocol (zero perception gaps), AIRAC 28-day cycle integration, cryptographic signing (Ed25519), fleet version synchronization, in-flight consistency, <500 MB/month fleet updates, $40-65K/14 weeks |
AV platform
| Topic | Primary | Supporting |
|---|---|---|
| NVIDIA Orin | 20-av-platform/compute/nvidia-orin-technical.md | 275 TOPS, 8 power modes, benchmarks |
| NVIDIA Thor | 20-av-platform/compute/nvidia-drive-thor.md | ~1000 TOPS, FP8, OEM commitments |
| TensorRT deployment | 20-av-platform/compute/tensorrt-deployment-guide.md | DLA, quantization, Lidar_AI_Solution |
| Hesai LiDAR | 20-av-platform/sensors/hesai-lidar.md | XT32, AT128 ASIL-B, FMC500 SoC |
| RoboSense LiDAR | 20-av-platform/sensors/robosense-lidar.md | RSHELIOS, RSBP, 7-sensor layout |
| 4D radar | 20-av-platform/sensors/4d-radar.md | Continental ARS548, weather immunity |
| Visible cameras | 20-av-platform/sensors/visible-cameras.md | Global vs rolling shutter, HDR/LFM, lens/FOV, trigger/PTP, ISP/RAW, cleaning, heating, and weather integration |
| IMU, GNSS, and RTK hardware | 20-av-platform/sensors/imu-gnss-rtk.md | Receiver/IMU classes, PPS/PTP wiring, antenna lever arms, correction transport, outage modes, spoofing/jamming health |
| Thermal/IR cameras | 20-av-platform/sensors/thermal-ir-cameras.md | FLIR Boson 640, LWIR fusion, night personnel, jet blast |
| Multi-LiDAR calibration | 20-av-platform/sensors/multi-lidar-calibration.md | Target-based + targetless (ICP, feature, learning-based), GTSAM-integrated online refinement, thermal drift compensation (-10C to +50C), PTP/PPS synchronization, overlap optimization for 4-8 RoboSense, calibration health monitoring, ISO 3691-4 traceability, 400-800h/year labor savings for 20+ vehicle fleet |
| Sensor-to-algorithm readiness | 20-av-platform/sensors/sensor-to-algorithm-readiness-contract.md | Pre-algorithm contract for calibration, timestamp, TF, preprocessing, health, provenance, and reject/degrade gates before perception, fusion, SLAM, tracking, occupancy, mapping, and planning consume sensor data |
| Sensor degradation & health monitoring | 20-av-platform/sensors/sensor-degradation-health-monitoring.md | Degradation taxonomy (optical/mechanical/environmental/electronic), 10 airside contamination sources, per-sensor diagnostics (LiDAR 7-check, radar SNR/coverage, thermal NUC/dead pixel, camera exposure/blur), cross-sensor consistency scoring, EMA-based temporal tracking with z-score anomaly, response matrix (4 sensors × 4 severity), fleet health analytics (zone correlation, seasonal patterns), predictive maintenance (linear extrapolation), cleaning schedules, 1 Hz ROS monitoring at <2ms, $35K/11 weeks |
| Automated sensor cleaning | 20-av-platform/sensors/automated-sensor-cleaning.md | Physical self-maintenance for 16-20 hr/day tarmac ops: cleaning modality comparison (air curtains, air burst, wipers, washer fluid, ultrasonic, heated windows, hydrophobic coatings, UV photocatalytic), contamination-to-cleaning mapping (de-icing glycol requires chemical cleaning — air jets spread it), per-sensor architecture (germanium thermal windows air-only, no wipers), health monitor closed-loop integration, power/weight budget (15-40W, 1.5-3.0 kg), $200-500/vehicle hardware, 15-25% availability improvement, 60-80% fewer depot cleaning visits |
| Solid-state LiDAR & photonics | 20-av-platform/sensors/solid-state-lidar-photonics.md | FMCW vs ToF measurement principles, silicon photonics integration (SiPh LiDAR-on-chip), OPA beam steering (GHz point-to-point, 0.01-0.05° angular resolution), MEMS mirror reliability, flash LiDAR for docking, per-point velocity (jet blast detection, zero-latency approaching-object detection), 1550nm eye safety (100x margin), Aeva Atlas/Voyant Helium/SiLC comparison, 50-200x longer MTBF (100K+ hrs), $150-450K/year fleet savings, Orin GTSAM velocity factor, adaptive resolution for active perception, phased migration strategy, $110-175K over 48 weeks |
| LiDAR ghost and multipath artifacts | 20-av-platform/sensors/lidar-ghost-multipath-artifacts.md | Wet surfaces, aircraft skins, glass, retroreflector bloom, sun/receiver saturation, multi-return ambiguity, and cross-sensor checks |
| Energy-efficient inference 24/7 | 20-av-platform/compute/energy-efficient-inference-24-7.md | Orin 15W/30W/50W power modes vs throughput, dynamic model switching (40-60% time in low-complexity), thermal throttling curves (-10C to +50C tarmac), battery-aware compute (SoC-correlated power budgets), DLA+GPU concurrent scheduling, per-model watt measurements, sleep/wake with <500ms wake-up, fleet-level energy optimization, 8-15% more daily operating hours, 12-18C lower junction temp |
| Edge-cloud hybrid inference | 20-av-platform/compute/edge-cloud-hybrid-inference.md | Three-tier architecture (on-vehicle Orin + airport MEC edge + cloud), model placement decision framework, split inference patterns, bandwidth/latency analysis, NVIDIA Triton on edge servers, graceful degradation (vehicle always autonomous), security, cost-benefit ($2,500/vehicle for shared edge vs $2,000-5,000 per Thor upgrade), industry approaches, airport advantage (bounded geography + private 5G) |
| Airport 5G | 20-av-platform/networking-connectivity/airport-5g-cbrs.md | 20-av-platform/networking-connectivity/airport-5g-case-studies.md |
| Deterministic networking (TSN) | 20-av-platform/networking-connectivity/deterministic-networking-tsn.md | IEEE 802.1 TSN standards (gPTP <100ns sync, TAS time-aware scheduling, FRER redundancy, frame preemption), mixed-criticality traffic classes (safety <100μs, sensors <5ms, best-effort), CAN bus migration (50-200x latency improvement for safety messages), zonal architecture, automotive TSN silicon (NXP SJA1110, Marvell 88Q6113), CAN-TSN gateway (NXP S32G3), 5G TSN bridge for V2X, Orin native TSN support, ASIL decomposition via TSN isolation, $230-440/vehicle hardware, $53-87K implementation |
Safety & certification
| Topic | Primary | Supporting |
|---|---|---|
| ISO 3691-4 | 60-safety-validation/standards-certification/iso-3691-4-deep-dive.md | 27 functions, $130K-380K |
| Full certification guide | 60-safety-validation/standards-certification/certification-guide.md | UL 4600, AMLAS, ISO 26262 |
| Regulatory trajectory | 80-industry-intel/regulations/regulatory-trajectory-deep-dive.md | FAA, EASA, CAAS, predicted timeline |
| Safety incidents | 60-safety-validation/safety-case/safety-incidents-lessons.md | Cruise, Waymo, Tesla, Uber ATG |
| Failure modes | 60-safety-validation/safety-case/failure-modes-analysis.md | SOTIF, hallucination taxonomy |
| Simplex architecture | 60-safety-validation/runtime-assurance/simplex-safety-architecture.md | RSS, OOD detection, ROS dual-stack |
| Ground crew safety | 70-operations-domains/airside/safety/ground-crew-pedestrian-safety.md | 27K accidents/yr, hi-vis paradox |
| Insurance & liability | 80-industry-intel/regulations/insurance-liability-airside.md | EU PLD, $35M exposure |
| Functional safety software | 60-safety-validation/standards-certification/functional-safety-software.md | MISRA C, ISO 26262 Part 6, static analysis, CI/CD, ROS safety patterns |
| Scenario taxonomy & edge cases | 60-safety-validation/verification-validation/airside-scenario-taxonomy.md | ISO 34502 adapted for airside, SOTIF hazard catalog (H1-H8+), 115 functional scenarios, ODD definition, Pegasus 6-layer, STPA, risk matrix, regulatory mapping |
| Testing & validation methodology | 60-safety-validation/verification-validation/testing-validation-methodology.md | V-model, scenario-based testing (ASAM OpenSCENARIO 2.0), coverage metrics (N-wise covering arrays), corner case/adversarial testing (CMA-ES falsification, LLM scenario generation, metamorphic testing), SIL/HIL/VIL, statistical safety (Zhao-Weng, Bayesian), shadow mode, regression/CI/CD, digital twin, airside test protocols, $105K first airport |
| LiDAR artifact removal validation | 60-safety-validation/verification-validation/robustness/lidar-artifact-removal-validation.md | Raw-vs-filtered evidence, do-not-delete hazard tests, weather/ghost/dynamic-object labels, localization observability, ODD degradation, and SOTIF argumentation |
| Airside dynamic map-cleaning benchmark | 60-safety-validation/verification-validation/airside-dynamic-map-cleaning-benchmark.md | False-deletion, false-retention, moved-object, FOD, construction, equipment, and localization-regression tests for map cleaning |
| Runtime verification & monitoring | 60-safety-validation/runtime-assurance/runtime-verification-monitoring.md | STL monitors (<1ms, 20 airside specs), OOD detection (energy+Mahalanobis+ensemble, 95-98% AUROC), maximally permissive shields (1-5% intervention), safety MCU (STM32H725), METAR ODD monitoring, WCET <5.5ms, ISO 26262 ASIL decomposition, UL 4600 compliance, DO-178C credit, fleet anomaly correlation, $115-200K/32 weeks |
| Online perception monitoring & ODD enforcement | 60-safety-validation/runtime-assurance/online-perception-monitoring-odd-enforcement.md | ML-specific silent degradation detection (domain shift, model staleness, adversarial natural conditions), input distribution monitoring (KL divergence, MMD on backbone features), output consistency checking (CUSUM/EWMA on detection counts, class distributions, tracking metrics), cross-modal consistency (LiDAR vs radar vs camera agreement), OOD detection integration (energy + Mahalanobis), ODD boundary state machine (NORMAL→DEGRADED→RESTRICTED→SUSPENDED with hysteresis), Perception Health Score (Bayesian fusion of monitors), calibration drift detection, temporal anomaly detection, <5ms total on Orin, ISO 3691-4/UL 4600/EU AI Act compliance |
| Formal verification of neural networks | 60-safety-validation/verification-validation/formal-verification-neural-networks.md | SMT/MILP complete verification (<100K params), alpha-beta-CROWN over-approximation (millions of params, VNN-COMP winner), IBP/SABR certified training, Lipschitz bounds for safety margins, layered strategy: complete for safety-critical (policy, CBF, Simplex), scalable for perception (PointPillars, CenterPoint), runtime for residual, ISO 3691-4/UL 4600/EU AI Act compliance |
| Fail-operational architecture | 60-safety-validation/runtime-assurance/fail-operational-architecture.md | 1oo2D, TMR, monitor-actuator patterns, dual-Orin compute, Orin FSI (DCLS R52), ASIL decomposition, sensor/actuator/power/CAN redundancy, degradation tiers, MRC planning, airside-specific (runway incursion HW geofence, jet blast hardening, EMI), $155-260K phased implementation |
| Weather-adaptive ODD management | 60-safety-validation/runtime-assurance/weather-adaptive-odd-management.md | 5-level ODD (A-E) with asymmetric transitions (fast degradation, slow recovery), METAR/TAF/ATIS automated parsing, on-vehicle environmental sensing (LiDAR return rate→visibility), fleet consensus, capability curves (sensor performance vs weather), continuous speed envelope, jet blast zone integration (ADS-B+thermal), seasonal adaptation profiles, dawn/dusk transition management, ISO 34502/21448/3691-4 compliance, EU AI Act transparency, $30-50K/8-12 weeks |
Planning, VLA & scene understanding
| Topic | Primary | Supporting |
|---|---|---|
| VLA for driving | 30-autonomy-stack/vla-vlm/vla-for-driving.md | Alpamayo, RT-2, PaLM-E, teacher-student distillation |
| Alpamayo setup | 30-autonomy-stack/vla-vlm/alpamayo-setup.md | Camera-only, non-commercial, 10B params |
| VLM scene understanding | 30-autonomy-stack/vla-vlm/vlm-scene-understanding.md | DriveVLM, DriveLM, NOTAM interpretation, turnaround assessment, FOD classification, VLM as 1-2Hz co-pilot |
| Spatial foundation models | 30-autonomy-stack/vla-vlm/spatial-foundation-models-airport.md | 4M unified multimodal, SpatialVLM spatial reasoning, RT-2/RT-X robotics transformers, Octo open-source policy, pi0 flow matching, HPT cross-embodiment, precision docking with spatial VLMs, gate identification, FOD detection/characterization, two-tier deployment (cloud+edge), distillation for Orin, in-context learning for new airports, Simplex integration, $55-95K phased |
| Neural motion planning | 30-autonomy-stack/planning/neural-motion-planning.md | SparseDrive, DiffusionDrive, GameFormer, Simplex safety integration |
| Frenet augmentation | 30-autonomy-stack/planning/frenet-planner-augmentation.md | Augmenting a classical Frenet planner |
| Motion prediction | 30-autonomy-stack/planning/motion-prediction.md | Trajectory prediction, interaction modeling |
| LLM reasoning for planning | 30-autonomy-stack/planning/llm-reasoning-planning.md | Chain-of-thought, interpretable decisions |
| Diffusion trajectory planning | 30-autonomy-stack/planning/diffusion-trajectory-planning.md | Diffusion-based motion generation |
| Safety-critical planning (CBF) | 30-autonomy-stack/planning/safety-critical-planning-cbf.md | Control Barrier Functions, CBF-QP safety filter, neural CBF synthesis, game-theoretic interaction (GameFormer, GIME), multi-agent CBFs (GCBF+), HJ reachability, CBF-Simplex integration, airside safety formulations |
| Neuro-symbolic scene graphs | 30-autonomy-stack/planning/neuro-symbolic-scene-graphs.md | Driving scene graphs, GNN interaction (LaneGCN, HiVT, HDGT), knowledge graphs for traffic rules, STL-constrained planning, compositional reasoning, LLM-symbolic hybrid, airside right-of-way encoding, NOTAM rule injection, interpretable decisions |
| Causal reasoning & counterfactuals | 30-autonomy-stack/planning/causal-reasoning-counterfactual.md | SCMs for driving, Pearl's 3 levels, counterfactual trajectory analysis, Halpern-Pearl causation, NOTEARS causal discovery, IRM cross-airport transfer, off-policy evaluation, LLM+SCM hybrid, KING counterfactual generation, EU PLD 2024/2853 compliance, causal ROS node at 2 Hz, $40-65K Phase 1+2 |
| RL driving policy | 30-autonomy-stack/planning/reinforcement-learning-driving-policy.md | CaRL (CoRL 2025 SOTA, PPO + simple rewards), IQL (best offline RL), SAC/TD3/TQC/CrossQ, BC→offline RL→online RL pipeline, safe RL (CPO, Lagrangian, CBF filter), privileged-to-sensor distillation, policy head <0.5ms Orin, $45-75K/32 weeks |
| Imitation learning & behavioral cloning | 30-autonomy-stack/planning/imitation-learning-behavioral-cloning.md | BC from teleop, MDN multimodal BC, Diffusion BC (DDIM 3-5 steps), DAgger with Frenet expert, MaxEnt IRL cost learning, GAIL, style-conditioned multi-operator BC, CBF safety filtering, Simplex integration, $35-55K/10-14 weeks |
| Joint prediction-planning | 30-autonomy-stack/planning/joint-prediction-planning.md | Predict-then-plan failure modes, PDM-Closed baseline, conditional prediction, game-theoretic (Stackelberg, level-K), contingency planning, occupancy flow scoring, NAVSIM/nuPlan benchmarks, Frenet planner augmentation with prediction costs, airside interaction modeling, 50-100ms on Orin |
| Autonomous docking & precision positioning | 30-autonomy-stack/planning/autonomous-docking-precision-positioning.md | Two-phase architecture (coarse Frenet → fine docking), visual servoing (IBVS/PBVS), LiDAR ICP template alignment (+-1-2cm), AprilTag fiducials (+-0.5cm at 2m), MPC docking controller (CasADi 2-5ms), impedance control for pushback contact, per-GSE tolerances (+-5cm belt loader to +-30cm fuel truck), third-generation tug crab steering advantage, safety PLC + personnel exclusion zones, 20 key takeaways, $53-90K/12-18 weeks |
Airport operations
| Topic | Primary | Supporting |
|---|---|---|
| Industry overview | 70-operations-domains/airside/operations/industry-overview.md | All competitors, regulatory gaps |
| Airport data APIs | 70-operations-domains/airside/operations/airport-data-integration.md | 70-operations-domains/airside/operations/airport-data-systems-detailed.md (real endpoints) |
| FOD & jet blast | 70-operations-domains/airside/operations/fod-and-jetblast.md | B737 148m zone, CFD tables |
| Turnaround prediction | 70-operations-domains/airside/operations/turnaround-prediction.md | Moonware HALO, Assaia |
| Pushback systems | 70-operations-domains/airside/operations/pushback-systems.md | Mototok, TaxiBot, WheelTug |
| Electric GSE market | 70-operations-domains/airside/operations/electric-gse-market.md | $2.8B→$5.2B, autonomy rankings |
| Aviation ecosystem | 70-operations-domains/airside/operations/aviation-ground-ops-ecosystem.md | Strategic context, business case |
| Battery & charging | 70-operations-domains/airside/operations/battery-charging-infrastructure.md | LiFePO4, 0.84yr payback, autonomous self-charging |
| Ground control instructions | 70-operations-domains/airside/operations/ground-control-instructions.md | A-CDM/A-SMGCS integration, D-TAXI, NOTAM parsing, marshaller gesture recognition, NLU, instruction-to-trajectory |
Deployment & operations
| Topic | Primary | Supporting |
|---|---|---|
| Deployment playbook | 70-operations-domains/deployment-playbooks/deployment-playbook.md | 4,500 lines, full checklists |
| Shadow mode | 60-safety-validation/verification-validation/shadow-mode.md | Tesla/Waymo/comma approaches |
| OTA & fleet management | 50-cloud-fleet/ota/ota-fleet-management.md | Canary deployment, A/B testing |
| Production ML | 40-runtime-systems/ml-deployment/production-ml-deployment.md | TensorRT, Triton, GPU reliability |
| Fleet dispatch | 50-cloud-fleet/fleet-management/fleet-management-dispatch.md | VRPTW, A-CDM triggers |
| Multi-airport adaptation | 70-operations-domains/deployment-playbooks/multi-airport-adaptation.md | AMDB bootstrapping, PointLoRA fine-tuning (500 labels), GNSS multipath mapping, 8-week onboarding, $75-150K per airport |
| HMI & operator interface | 40-runtime-systems/monitoring-observability/hmi-operator-interface.md | Dashboard design, trust calibration, 4-mode control, handoff procedures, operator training, incident reporting |
| Teleoperation | 40-runtime-systems/monitoring-observability/teleoperation-systems.md | Fernride, Waymo 1:41 ratio |
| Workforce transition | 70-operations-domains/deployment-playbooks/workforce-transition.md | 1.5-2M workers affected, union considerations, retraining |
| CI/CD & DevOps pipeline | 40-runtime-systems/ml-deployment/av-cicd-devops-pipeline.md | End-to-end AV CI/CD: code CI (MISRA, static analysis), ML model CI (DVC, TensorRT optimization), SIL/HIL/VIL simulation gates, map/config CI, artifact versioning, fleet deployment (canary, staged rollout), ML regression detection, safety assurance integration, airside-specific pipeline requirements, industry approaches |
| Fleet TCO & business case | 70-operations-domains/airside/business-case/fleet-tco-business-case.md | Per-vehicle CAPEX ($95-210K floor), OPEX breakdown, 3-shift labor savings ($150K/year), NPV $45-80M at 200 vehicles, break-even Year 2-4, RaaS $10-14K/month, certification cost $530K-1.95M across 5 jurisdictions, UISEE 40-60% cost advantage threat, airport cluster strategy |
| EV fleet energy co-optimization | 50-cloud-fleet/fleet-management/ev-fleet-energy-co-optimization.md | Joint charging-routing-task EVRP optimization, LiFePO4 degradation models (cycle counting, throughput, temperature), optimal C-rate selection, V2G for airports (~1-2 MWh dispatchable storage, $50-200/MWh demand response), grid-aware scheduling (demand charge management), stochastic EVRP under uncertainty, MILP/RL/MPC approaches, OCPP 2.0.1 integration |
| Fleet anomaly root-cause attribution | 50-cloud-fleet/observability/fleet-anomaly-root-cause-attribution.md | Automated causal attribution for fleet-level anomalies: CUSUM/EWMA statistical monitoring, hierarchical anomaly detection (fleet→airport→vehicle→subsystem), causal discovery (NOTEARS, PC algorithm), Shapley-value attribution, Bayesian diagnosis trees, OTA regression detection, map staleness attribution, environmental correlation, streaming pipeline (Kafka/Flink), MTTR reduction |
| Fleet predictive maintenance | 50-cloud-fleet/fleet-management/fleet-predictive-maintenance.md | PHM framework (ISO 13381, 4-level architecture), Weibull failure analysis (LiDAR β=1.8-2.2/25-40K hrs, motors β=3.5/40-60K hrs), correlated failure modes (de-icing, salt spray, heat events), ML prediction (LSTM/XGBoost/autoencoder anomaly), multi-echelon spare parts inventory (4-level), cold-start sizing for new airports, joint maintenance-operations scheduling (CP-SAT), fleet availability modeling (95%+ vehicle, 98%+ fleet), seasonal profiles, ROS diagnostics integration, $7-19.5K/vehicle/year maintenance cost, 30-40% cost reduction with predictive vs reactive, $50-80K implementation |
Foundation overview entry points
| Topic | Primary | Scope |
|---|---|---|
| Probability and statistics foundations | 10-knowledge-base/probability-statistics/overview.md | Uncertainty, likelihoods, covariance, gates, robust statistics, calibration, and decision thresholds |
| Optimization foundations | 10-knowledge-base/optimization/overview.md, Nonlinear Solver Diagnostics Crosswalk | Residual objectives, Jacobians, manifold linearization, globalization, solver patterns, and failure triage across residual, scaling, damping, rank, covariance, and backend causes |
| Numerical linear algebra foundations | 10-knowledge-base/numerical-linear-algebra/overview.md, Sparse Estimation Backend Crosswalk, Nonlinear Solver Diagnostics Crosswalk | Factorization, conditioning, rank, sparsity, Schur complements, marginalization, covariance recovery, and sparse backend triage |
| State estimation foundations | 10-knowledge-base/state-estimation/overview.md | Filtering, smoothing, fusion, association, observability, integrity, and deployed estimator lifecycle |
| Geometry and sensor foundations | 10-knowledge-base/geometry-3d/overview.md | Frames, projection, Lie groups, registration, calibration, and sensor geometry |
| Mapping foundations | 10-knowledge-base/mapping/overview.md | Occupancy, semantic layers, volumetric maps, fusion policy, dynamic/static separation, and map QA |
| Sensor foundations | 10-knowledge-base/sensors/overview.md | Measurement likelihoods, error budgets, observability limits, degradation modes, and modality handoff assumptions |
| Sensor readiness handoff | 20-av-platform/sensors/sensor-to-algorithm-readiness-contract.md | Operational bridge from sensor foundations into algorithm input acceptance gates |
| Signal processing foundations | 10-knowledge-base/signal-processing/overview.md | Sampling, filtering, FFT, radar processing, CFAR, aliasing, windowing, and clutter contracts |
| Controls foundations | 10-knowledge-base/controls/overview.md | Closed-loop tracking, vehicle dynamics, MPC/iLQR, constraints, actuator limits, and safety filters |
| Robotics foundations | 10-knowledge-base/robotics/overview.md | Robot/task vocabulary, route/behavior/motion-planning boundaries, Lanelet2 concepts, and embodiment assumptions |
| Systems engineering foundations | 10-knowledge-base/systems-engineering/overview.md | Timing, latency, validation metrics, release gates, observability, architecture contracts, and evidence flow |
| Machine learning foundations | 10-knowledge-base/machine-learning/overview.md | Learned representations, objectives, architectures, self-supervision, world models, evaluation, and deployment failure modes |
Mathematical foundations
| Topic | Primary |
|---|---|
| PointPillars | 10-knowledge-base/geometry-3d/pointpillars.md — tensor shapes, TensorRT |
| VQ-VAE / FSQ | 10-knowledge-base/machine-learning/vqvae-tokenization.md — straight-through estimator, codebook collapse |
| Transformers | 10-knowledge-base/machine-learning/transformer-world-models.md — causal attention, KV-cache, scaling laws |
| Diffusion models | 10-knowledge-base/machine-learning/diffusion-models.md — DDPM, DiT, flow matching |
| GTSAM | 10-knowledge-base/state-estimation/gtsam-factor-graphs.md — ISAM2, VGICP, neural factors |
| Probability and uncertainty | 10-knowledge-base/probability-statistics/gaussian-noise-covariance-information.md, 10-knowledge-base/probability-statistics/mahalanobis-chi-square-gating.md, 10-knowledge-base/probability-statistics/likelihood-map-mle-least-squares.md — Gaussian noise, covariance/information matrices, whitening, Mahalanobis gates, chi-square thresholds, NIS/NEES, likelihoods, MLE, MAP, and least-squares foundations |
| Robust statistics and multimodal beliefs | 10-knowledge-base/probability-statistics/robust-statistics-ransac-hypothesis-testing.md, 10-knowledge-base/probability-statistics/robust-losses-m-estimators-huber-cauchy-tukey-geman-mcclure.md, 10-knowledge-base/probability-statistics/mixture-models-multimodal-beliefs.md — RANSAC, hypothesis tests, Huber/Cauchy/Tukey/Geman-McClure M-estimators, Gaussian mixtures, mixture reduction, and multi-hypothesis localization/tracking |
| Graphical models and information theory | 10-knowledge-base/probability-statistics/probabilistic-graphical-models-message-passing.md, 10-knowledge-base/probability-statistics/information-theory-for-perception-ml.md — factor graphs, Bayes nets, message passing, entropy, mutual information, KL divergence, active perception, and representation objectives |
| Calibration and uncertainty guarantees | 10-knowledge-base/probability-statistics/uncertainty-quantification-calibration-conformal.md — calibration error, reliability diagrams, conformal prediction, prediction sets, and safety-facing uncertainty contracts |
| Nonlinear optimization | Nonlinear Solver Diagnostics Crosswalk, 10-knowledge-base/optimization/objective-residual-design-and-audit.md, 10-knowledge-base/optimization/solver-selection-and-convergence-diagnosis.md, 10-knowledge-base/optimization/nonlinear-least-squares-first-principles.md, 10-knowledge-base/optimization/gauss-newton-levenberg-marquardt-dogleg.md, 10-knowledge-base/optimization/trust-region-line-search-globalization.md, 10-knowledge-base/optimization/jacobians-autodiff-manifold-linearization.md, 10-knowledge-base/optimization/factor-graph-solver-patterns-ceres-gtsam-g2o.md — residuals, whitening, Gauss-Newton, LM, dogleg, globalization, autodiff, manifold linearization, solver-library tradeoffs, and solver-failure triage |
| Numerical linear algebra | Sparse Estimation Backend Crosswalk, Nonlinear Solver Diagnostics Crosswalk, 10-knowledge-base/numerical-linear-algebra/cholesky-ldlt-normal-equations.md, 10-knowledge-base/numerical-linear-algebra/qr-svd-rank-revealing-solvers.md, 10-knowledge-base/numerical-linear-algebra/eigenvalues-hessian-conditioning-observability.md, 10-knowledge-base/numerical-linear-algebra/sparse-matrices-fill-in-ordering.md, 10-knowledge-base/numerical-linear-algebra/square-root-information-and-covariance-recovery.md, 10-knowledge-base/numerical-linear-algebra/schur-complement-marginalization-pcg.md — Cholesky/LDLT, QR/SVD, rank, nullspaces, sparse fill-in, orderings, square-root information, Schur complements, marginalization, PCG, and backend diagnostics |
| Geometry and mapping foundations | 10-knowledge-base/geometry-3d/lie-groups-se3-so3-jacobians.md, 10-knowledge-base/geometry-3d/camera-projective-geometry-pnp-triangulation.md, 10-knowledge-base/geometry-3d/point-cloud-registration-math-icp-ndt-gicp.md, 10-knowledge-base/geometry-3d/correspondence-search-data-structures.md, 10-knowledge-base/mapping/occupancy-bayes-evidential-dynamic-grids.md, 10-knowledge-base/geometry-3d/geodesy-map-projections-datums.md — Lie groups, projective geometry, PnP, triangulation, ICP/GICP/NDT, correspondence search, occupancy Bayes updates, and geodesy |
| Association, filters, and signals | 10-knowledge-base/state-estimation/data-association-and-gating.md, 10-knowledge-base/state-estimation/probabilistic-multi-object-association.md, 10-knowledge-base/state-estimation/information-filters-and-smoothers.md, 10-knowledge-base/state-estimation/particle-filters-and-hypothesis-management.md, 10-knowledge-base/sensors/sensor-likelihoods-noise-error-budgets.md, 10-knowledge-base/signal-processing/sampling-fft-windowing-filtering.md, 10-knowledge-base/signal-processing/radar-ambiguity-chirp-design-doppler-limits.md, 10-knowledge-base/signal-processing/cfar-detection-thresholding.md, 10-knowledge-base/signal-processing/sensor-filtering-alpha-beta-kalman-complementary.md, 10-knowledge-base/systems-engineering/time-sync-ptp-timestamping-latency-models.md, 10-knowledge-base/systems-engineering/benchmarking-metrics-statistical-validity.md — assignment, JPDA/MHT/RFS, information filters, particle filters, sensor likelihoods, FFT/filtering, radar ambiguity, CFAR, simple filters, timestamping, and statistical validity |
| Sensor measurement models | 10-knowledge-base/geometry-3d/lidar-working-principles-noise-models.md, 10-knowledge-base/geometry-3d/camera-imaging-noise-calibration.md, 10-knowledge-base/signal-processing/radar-fmcw-mimo-doppler.md — LiDAR, camera, and radar physics, noise, covariance, and calibration implications |
| IMU, GNSS, and wheel odometry | 10-knowledge-base/state-estimation/imu-error-models-preintegration.md, 10-knowledge-base/state-estimation/gnss-rtk-error-models.md, 10-knowledge-base/state-estimation/wheel-odometry-encoder-models.md — propagation, preintegration, RTK factors, dead reckoning, covariance, and outage behavior |
| Timing and calibration observability | 10-knowledge-base/systems-engineering/time-synchronization-error-budgets.md, 10-knowledge-base/geometry-3d/multi-sensor-calibration-observability.md — timestamp error budgets, PTP/PPS, hand-eye calibration, observability motions, and online health checks |
| Event and thermal cameras | 10-knowledge-base/geometry-3d/event-thermal-camera-models.md — event camera contrast model, timestamp noise, thermal radiometry, NUC, and low-light/perception transfer |
| Lanelet2 | 10-knowledge-base/robotics/lanelet2-maps.md — airport extensions, AIXM conversion |
| Frenet planning | 10-knowledge-base/controls/frenet-trajectory-math.md — Werling 2010, quintic polynomials |
| Constrained control and belief-space decision-making | 10-knowledge-base/controls/constrained-optimization-mpc-ilqr-first-principles.md, 10-knowledge-base/controls/mdp-pomdp-belief-space-rl-first-principles.md — KKT conditions, MPC, iLQR, MDPs, POMDPs, belief states, and RL interfaces for learned autonomy |
| RTK/GPS/IMU | 10-knowledge-base/state-estimation/rtk-gps-imu-localization.md — preintegration, NTRIP |
| Mamba SSM | 10-knowledge-base/machine-learning/mamba-ssm-for-driving.md — DriveMamba, O(n) vs O(n²) |
| Theory | 10-knowledge-base/systems-engineering/theoretical-foundations.md — POMDP, free energy, PAC bounds |
| Architecture | 10-knowledge-base/systems-engineering/architecture-innovations.md — MoE, DiT, flow matching, FSQ |
| Sparse attention for 3D | 10-knowledge-base/machine-learning/sparse-attention-3d-perception.md — PTv3 serialized attention (80.4% mIoU, 3x faster), FlatFormer flattened windows (4.6x faster than SST), LitePT (CVPR 2026, 3.6x fewer params), SparseOcc, deformable attention, FlashAttention on Orin, TensorRT custom ops, hybrid SpConv+attention, multi-LiDAR cross-attention, window size 256-512 optimal for Orin |
Runtime, fleet, and validation topics
| Topic | Primary | Supporting |
|---|---|---|
| Sensor fusion | 30-autonomy-stack/perception/overview/sensor-fusion-architectures.md | |
| Synthetic data | 50-cloud-fleet/data-platform/synthetic-data-generation.md | |
| Evaluation benchmarks | 60-safety-validation/verification-validation/evaluation-benchmarks.md | |
| nuScenes/Waymo guide | 30-autonomy-stack/perception/datasets-benchmarks/nuscenes-waymo-practical-guide.md | |
| Transfer learning | 50-cloud-fleet/mlops/transfer-learning.md | |
| ROS 2 migration | 40-runtime-systems/ros-autoware/ros2-migration.md | |
| Autoware Universe | 40-runtime-systems/ros-autoware/autoware-universe-deep-dive.md | |
| Open-source ecosystem | 40-runtime-systems/ml-deployment/opensource-ecosystem.md | |
| Embodied AI crossover | 10-knowledge-base/robotics/embodied-ai-crossover.md | |
| Data engine from bags | 50-cloud-fleet/data-platform/data-engine-from-bags.md | |
| Continual learning | 50-cloud-fleet/mlops/continual-learning.md | |
| 3D annotation tools | 50-cloud-fleet/data-platform/3d-annotation-tools.md | |
| Isaac ROS for airside | 40-runtime-systems/ros-autoware/isaac-ros-for-airside.md | |
| Test-time adaptation | 30-autonomy-stack/perception/overview/test-time-adaptation-airside.md | TENT, CoTTA, SAR, SFDA, OOD detection, active learning, multi-airport deployment |
| Test-time training for airport onboarding | 30-autonomy-stack/perception/overview/test-time-training-airport-onboarding.md | TTT vs TTA distinction (gradient-based auxiliary tasks), TTT++ multi-head, TTT-MAE, TTT layers as RNN, online LoRA with MAE loss, LiDAR-specific TTT (point cloud MAE), safety-bounded TTT on Orin (compute budget), catastrophic forgetting prevention (EWC, anchor loss), Simplex integration (TTT as AC, frozen as BC), airport onboarding protocol, comparison with PointLoRA, $25-45K/10-14 weeks |
| Fleet data pipeline | 50-cloud-fleet/data-platform/fleet-data-pipeline.md | RosBag management, DVC versioning, labeling workflows, fleet telemetry, storage costs |
| Data flywheel (closed-loop) | 50-cloud-fleet/mlops/data-flywheel-airside.md | Trigger-based collection (50GB/day budget), auto-labeling (70-85% cost reduction), active learning (40-50% fewer labels), model training orchestration, A/B testing, scenario mining, multi-airport LoRA, flywheel breakeven ~Month 18, mAP trajectory 45%→82% over 24 months |
| Radar-LiDAR fusion for adverse weather | 30-autonomy-stack/perception/overview/radar-lidar-fusion-adverse-weather.md | L4DR (AAAI 2025, +20% mAP dense fog), Continental ARS548 4D radar ($500-1500), asymmetric mid-level fusion (LiDAR-primary, radar-augmented), radar-guided densification, cross-attention LiDAR→radar, adaptive fusion gating (weather-aware weights), de-icing spray detection, track-level Kalman fusion, 4-mode degradation management (NORMAL→EMERGENCY), ROS integration, $35-55K/12 weeks |
| Federated learning (fleet) | 50-cloud-fleet/mlops/federated-learning-fleet.md | FedAvg/FedProx/SCAFFOLD, hybrid centralized+federated LoRA (97% comm reduction), FedBN for multi-airport, on-vehicle Orin LoRA training, DP privacy (epsilon 10-50), Byzantine-robust FLTrust, federated continual learning, hierarchical aggregation, FedDF for heterogeneous models, Flower/FLARE, break-even ~10 airports, $130K/year at 50 airports vs $1.3M centralized |
| LiDAR data augmentation | 50-cloud-fleet/mlops/lidar-data-augmentation.md | GT-database sampling (+15-25% rare class AP), 3D copy-paste, PolarMix (CVPR 2022, +3-7% mAP), LaserMix (CVPR 2023), LiDAR corruptions (rain/fog/beam dropout/de-icing), intensity augmentation, class-balanced sampling with safety priority, cross-airport GT database sharing, 40-60% labeling reduction ($15-45K savings/airport) |
| Cloud backend infrastructure | 50-cloud-fleet/data-platform/cloud-backend-infrastructure.md | Fleet data backend: three-zone data lake (Raw/Bronze → Processed/Silver → Curated/Gold), S3 event-driven ingestion (Lambda), streaming telemetry (MQTT→IoT Core→TimeStream→Grafana), Apache Airflow DAG catalog (7 DAGs), rosbag processing K8s jobs, feature store (Feast), auto-labeling pipeline integration, map construction data flow, multi-airport data isolation, cost modeling ($200-460/vehicle/month), monitoring/observability, $80-135K/28 weeks |
| On-vehicle data triage & upload | 40-runtime-systems/data-logging/on-vehicle-data-triage-selective-upload.md | Vehicle-side data management: multi-tier ring buffers (LiDAR/camera/IMU/CAN/GTSAM, NVMe 1-4TB), event-triggered clip extraction (safety events, perception anomalies, localization failures, operator flags), edge scenario classification (lightweight CNN/DLA), bandwidth-aware upload scheduling (priority queue, 50GB/day budget), compression (LZ4 point clouds, H.265 camera, delta poses), rosbag split/trim/mcap, active learning integration, fleet upload coordination (deduplication, coverage diversity), GDPR camera data handling, ROS node architecture |
Synthesis & strategy
| Topic | Primary |
|---|---|
| Master synthesis | 90-synthesis/master/master-synthesis.md — Executive summary, tiered recommendations |
| Design spec | 90-synthesis/decisions/design-spec.md — 891-line Simplex architecture |
| POC proposals | 90-synthesis/poc-roadmaps/poc-proposals.md — 8 models with code and costs |
| Competitive landscape | 80-industry-intel/market-competitive/competitive-landscape.md — All players compared, strategic quadrant |
| Technology readiness | 90-synthesis/readiness-risk/technology-readiness.md — TRL per POC, go/no-go criteria |
| Knowledge gap backlog | 90-synthesis/readiness-risk/knowledge-gap-backlog.md — P0/P1/P2 missing research files across the end-to-end AV architecture |
| Active frontier source registry | 90-synthesis/readiness-risk/active-frontier-source-registry.md — Manual source and query registry for perception, SLAM, world models, VLA/VLM, datasets, and validation monitoring |
| Getting started | 90-synthesis/master/getting-started.md — Day 1 guide with runnable code |
Recently Added (Latest Sessions)
| Document | Key Contribution |
|---|---|
20-av-platform/sensors/sensor-to-algorithm-readiness-contract.md | Bridge contract for sensor acquisition, calibration, synchronization, preprocessing, health, provenance, and algorithm input acceptance before perception/SLAM/fusion consumers run |
90-synthesis/readiness-risk/active-frontier-source-registry.md | Manual-first registry of active frontier sources, native filters, query patterns, canonical routing rules, and semi-automation boundaries |
| Web gap expansion wave | 31 source-backed files covering 4D radar-camera occupancy, CVFusion, FMCW LiDAR predictive detection, cross-domain scene flow, TrackOcc, dynamic 3DGS/4DGS, DistillNeRF, self-supervised occupancy flow, UniScene, robust/certifiable SLAM backends, lifelong map maintenance, GPR/radar localization, probability/control foundations, adverse/OOD/FOD datasets, and validation protocols |
| Perception/SLAM reliability gap wave | 36 source-backed files covering occupancy fusion, dynamic/free-space occupancy, radar-LiDAR adverse-weather detection, RobuRCDet, SAMFusion, STU, synthetic FOD, OVAD/OVODA, open-vocabulary panoptic occupancy, RCP-Bench, V2X sequential datasets, Scan Context, LiDAR BA factors, Kimera-Multi, COVINS/COVINS-G, D2SLAM, UWB/range SLAM, OKVIS2-X, MM-LINS, event/thermal/radar localization, continuous-time and volumetric-map foundations, detection/tracking foundations, fleet-data contracts, and perception/SLAM/map validation protocols |
| First-principles foundations wave | 37 source-backed KB files covering Gaussian noise, Mahalanobis/chi-square gating, MAP/MLE, robust statistics, mixtures, Gauss-Newton, LM, dogleg, Jacobians, residual audits, solver convergence diagnosis, Ceres/GTSAM/g2o, Cholesky/LDLT, QR/SVD, sparse solvers and backend crosswalks, square-root information, Schur/PCG, Lie groups, projective geometry, ICP/GICP/NDT, occupancy grids, geodesy, assignment, JPDA/MHT/RFS, filters, sensor likelihoods, signal processing, radar ambiguity, CFAR, timestamping, and statistical benchmarking |
90-synthesis/readiness-risk/continuous-research-loop.md | Continuous research loop for discovery, triage, atomic-file promotion, cross-linking, verification, and next-queue selection across perception, SLAM, sensors, and mapping |
| Perception/SLAM/sensor deep-dive wave | 33 source-backed files covering SplatAD and Gaussian/4DGS perception, latest sparse/radar-camera perception, production LIVO/SLAM, Gaussian/radar SLAM, and sensor measurement/noise fundamentals |
10-knowledge-base/, 20-av-platform/, 30-autonomy-stack/, 40-runtime-systems/, 50-cloud-fleet/, 60-safety-validation/, 70-operations-domains/ P0 gap wave | 35 source-backed P0 gap files covering foundations, platform power/diagnostics/ruggedization, planning/control/V2X, E2E/VLA/world models, runtime/cloud operations, safety evidence, and non-airside operations domains |
90-synthesis/readiness-risk/knowledge-gap-backlog.md | Cross-architecture gap backlog from parallel research agents: P0/P1/P2 missing files across foundations, platform, autonomy, runtime/cloud, safety, operations, and industry intelligence |
30-autonomy-stack/localization-mapping/overview/production-lidar-map-localization.md | Production scan-to-map matching: VGICP/NDT/ICP comparison, multi-resolution coarse-to-fine, eigenvalue degeneracy detection, multi-LiDAR fusion strategies, GTSAM adaptive noise, 5-level fallback, GeoTransformer cold start, 15-25ms Orin, $30-53K |
40-runtime-systems/data-logging/on-vehicle-data-triage-selective-upload.md | Vehicle-side data management: ring buffers (NVMe 1-4TB), event-triggered clips (safety/perception/localization), edge scenario classification, bandwidth-aware upload (50GB/day), compression, rosbag/mcap, fleet upload coordination, active learning integration |
60-safety-validation/runtime-assurance/online-perception-monitoring-odd-enforcement.md | ML silent degradation detection: input distribution monitoring, output consistency (CUSUM/EWMA), cross-modal agreement, OOD integration, ODD state machine with hysteresis, Perception Health Score, calibration drift, temporal anomaly, <5ms on Orin |
30-autonomy-stack/localization-mapping/maps/map-tile-versioning-distribution.md | Map distribution lifecycle: spatial tiling, content-addressable versioning (Merkle tree), differential updates (2-8% of full tile), atomic swap protocol, AIRAC integration, cryptographic signing, fleet synchronization, <500 MB/month |
50-cloud-fleet/fleet-management/ev-fleet-energy-co-optimization.md | Joint EV fleet energy co-optimization: EVRP formulation, LiFePO4 degradation, V2G demand response ($50-200/MWh), grid-aware scheduling, stochastic optimization, MILP/RL/MPC, OCPP 2.0.1 |
30-autonomy-stack/multi-agent-v2x/ramp-traffic-conflict-deadlock-prevention.md | Ramp traffic coordination: zone-capacity graph, reservation protocol, wait-die deadlock prevention, 9-level priority, stand sequencing, V2X fallback, MAPF (CBS/PIBT), $50-75K |
30-autonomy-stack/perception/overview/test-time-training-airport-onboarding.md | TTT for rapid airport onboarding: gradient-based auxiliary tasks, TTT-MAE, online LoRA, safety-bounded on Orin, catastrophic forgetting prevention, Simplex integration |
50-cloud-fleet/observability/fleet-anomaly-root-cause-attribution.md | Fleet anomaly attribution: CUSUM/EWMA monitoring, causal discovery (NOTEARS), Shapley values, Bayesian diagnosis, OTA regression, map staleness, environmental correlation |
50-cloud-fleet/data-platform/cloud-backend-infrastructure.md | Fleet data backend: three-zone data lake, S3+Lambda ingestion, MQTT streaming telemetry, Airflow orchestration, rosbag K8s processing, Feast feature store, auto-labeling, multi-airport isolation, $200-460/vehicle/month |
30-autonomy-stack/localization-mapping/maps/map-construction-pipeline.md | End-to-end HD map construction: survey drives → multi-session SLAM → alignment → annotation → Lanelet2 → QA → deployment. 5-7 days at $20-35K per airport, AMDB bootstrap, SAM+CLIP auto-annotation |
20-av-platform/compute/edge-cloud-hybrid-inference.md | Three-tier compute (vehicle+edge+cloud): model placement, split inference, graceful degradation, Triton edge server, $2,500/vehicle shared edge, airport private 5G advantage |
20-av-platform/sensors/sensor-to-algorithm-readiness-contract.md | Sensor readiness contract: acquisition timestamps, calibration package, frame tree, preprocessing, health state, provenance, modality-specific checks, algorithm handoff table, reject/degrade rules, and evidence artifacts |
20-av-platform/sensors/automated-sensor-cleaning.md | Physical self-maintenance: air curtains + burst + washer + wiper + heated windows, contamination mapping, germanium-safe thermal cleaning, health monitor closed-loop, $200-500/vehicle, 15-25% availability gain |
20-av-platform/sensors/solid-state-lidar-photonics.md | Solid-state LiDAR: FMCW per-point velocity, silicon photonics OPA, Voyant Helium/Aeva Atlas/SiLC comparison, 100K+ hr MTBF, 1550nm eye safety, $150-450K/year fleet savings, phased migration strategy |
20-av-platform/networking-connectivity/deterministic-networking-tsn.md | Deterministic networking: IEEE 802.1 TSN (gPTP <100ns, TAS scheduling, FRER redundancy), safety messages <10μs (50-200x faster than CAN), mixed-criticality scheduling, CAN-TSN gateway, 5G TSN bridge, $230-440/vehicle |
30-autonomy-stack/vla-vlm/spatial-foundation-models-airport.md | Spatial foundation models: 4M, SpatialVLM, RT-2/Octo/pi0 for airport robotics, precision docking, FOD characterization, two-tier cloud+edge deployment, distillation for Orin |
50-cloud-fleet/fleet-management/fleet-predictive-maintenance.md | Fleet predictive maintenance: PHM framework, Weibull failure models, correlated airside failures, ML prediction, multi-echelon spare parts, cold-start sizing, joint scheduling, fleet availability modeling, 30-40% cost reduction |
30-autonomy-stack/planning/imitation-learning-behavioral-cloning.md | IL for airside: BC from teleop (BEV+GRU), MDN multimodal, Diffusion BC (DDIM 3-5 steps, 15-30ms Orin), DAgger with Frenet expert, MaxEnt IRL cost learning, GAIL, style-conditioned multi-operator, CBF post-processing, Simplex three integration modes, $35-55K |
30-autonomy-stack/planning/joint-prediction-planning.md | Joint prediction-planning: PDM-Closed baseline, conditional prediction, game-theoretic (Stackelberg, level-K), contingency planning, occupancy flow scoring, NAVSIM/nuPlan, Frenet augmentation with prediction costs (70-80% benefit at 10% cost) |
60-safety-validation/runtime-assurance/fail-operational-architecture.md | Fail-operational HW redundancy: 1oo2D, TMR, monitor-actuator, dual-Orin + FSI (DCLS R52 ASIL D), ASIL decomposition (ASIL B(D) + ASIL B(D)), sensor/actuator/power/CAN redundancy, degradation tiers (T0-T5), MRC planning, runway incursion HW geofence, $155-260K phased |
40-runtime-systems/ml-deployment/av-cicd-devops-pipeline.md | AV CI/CD pipeline: code CI (MISRA/static analysis), ML model CI (DVC/TensorRT), SIL/HIL/VIL simulation gates, map/config CI, artifact versioning, fleet deployment (canary rollout), ML regression detection, safety assurance, airside-specific requirements |
30-autonomy-stack/multi-agent-v2x/fleet-task-allocation-scheduling.md | Fleet GSE scheduling: MILP/CP-SAT optimal, CBBA decentralized, A-CDM predictive, RL dispatch, charging-aware, multi-objective |
60-safety-validation/runtime-assurance/weather-adaptive-odd-management.md | 5-level ODD with METAR/TAF/sensor fusion, capability curves, continuous speed envelope, jet blast zones, seasonal profiles |
30-autonomy-stack/localization-mapping/overview/robust-state-estimation-multi-sensor.md | ESKF deep dive, chi-squared gating, multi-hypothesis IMM, GPS-denied budgets, fleet state consistency, <0.5ms on Orin |
30-autonomy-stack/localization-mapping/maps/realtime-occupancy-grid-mapping.md | Log-odds occupancy, GPU raycasting, multi-LiDAR fusion, nvblox/VDBFusion, TSDF/ESDF, fleet-shared grids, costmap for Frenet |
50-cloud-fleet/data-platform/fleet-data-pipeline.md | End-to-end fleet data: 200GB/day/vehicle, DVC versioning, rosbag processing, labeling workflows ($15-45/frame), fleet telemetry (Grafana), storage tiers, 5-100 vehicle scaling |
30-autonomy-stack/simulation/sim-to-real-transfer-airside.md | Sim-to-real for airside: LiDAR simulation fidelity, domain randomization, UniSim/LidarDM, curriculum learning, reality gap measurement, CARLA/Isaac airport env, $50-75K first airport |
30-autonomy-stack/perception/overview/test-time-adaptation-airside.md | TTA/domain adaptation for multi-airport: TENT, CoTTA, SAR, SFDA (SHOT/NRC), OOD triggers, active learning, LiDAR-specific adaptation, fleet-scale strategy, per-airport cost |
30-autonomy-stack/perception/overview/lidar-semantic-segmentation.md | LiDAR segmentation SOTA: Cylinder3D, FlatFormer, PTv3, SalsaNext; ALPINE training-free panoptic; 18-class airside taxonomy; Orin real-time (18-35ms); PointLoRA fine-tuning path |
30-autonomy-stack/perception/overview/model-compression-edge-deployment.md | Unified compression guide: PTQ/QAT quantization, knowledge distillation (TinyBEV), structured pruning, ModelOpt, per-model Orin recipes, 5-15x speedup at 1-3% accuracy loss |
30-autonomy-stack/perception/overview/multi-object-tracking.md | 3D MOT for airside: CenterPoint tracker, SimpleTrack, MCTrack, HOTA metrics, airside Re-ID (tail numbers, fleet IDs), ROS integration, 10Hz on Orin |
30-autonomy-stack/world-models/occupancy-deployment-orin.md | Occupancy on Orin: FlashOcc TensorRT (197 FPS), SparseOcc, LiDAR voxelization, nvblox ROS bridge, multi-resolution strategy, INT8 calibration |
20-av-platform/sensors/thermal-ir-cameras.md | Thermal cameras for airside: LWIR/MWIR bands, FLIR Boson 640 vs Seek Mosaic, night personnel detection, jet blast visualization, Orin MIPI integration, $8-22K/vehicle |
30-autonomy-stack/planning/neural-motion-planning.md | Neural/learned motion planning SOTA (2023-2026): IL planners (PlanTF, UniAD, VAD, SparseDrive, GenAD, Diffusion-Planner), game-theoretic (GameFormer, MARC), differentiable optimization (DIPP, DTPP), VLA planning (DriveVLM, Alpamayo, PlanAgent), safety (CBF, RSS, SafeDreamer, Simplex), NAVSIM benchmark, Orin deployment |
30-autonomy-stack/localization-mapping/maps/hd-map-standards-airside.md | OpenDRIVE, AMDB/AMXM, NDS comparison; AMXM→Lanelet2 pipeline; NOTAM integration; cost estimates |
30-autonomy-stack/localization-mapping/maps/neural-online-mapping-sota.md | MapTracker (+69% consistency), StreamMapNet, NMP, topology reasoning (TopoMLP, LaneSegNet), airside adaptation strategy |
30-autonomy-stack/perception/overview/infrastructure-cooperative-perception.md | V2I fusion for airports, V2X-ViT/Where2comm/CoBEVT, existing airport systems (SMR/MLAT/ADS-B/CCTV), 0.5-1.5yr payback |
30-autonomy-stack/perception/overview/lidar-foundation-models.md | PTv3/Sonata/ScaLR, pre-training saves 50-80% labels, FlatFormer real-time on Orin, PointLoRA for fine-tuning |
30-autonomy-stack/localization-mapping/overview/lidar-slam-algorithms.md | KISS-ICP, LIO-SAM, FAST-LIO2, Point-LIO comparison; degeneracy detection; airside algorithm selection |
60-safety-validation/cybersecurity/cybersecurity-airside-av.md | Threat models, ISO/SAE 21434, EASA requirements, sensor security, incident response |
70-operations-domains/deployment-playbooks/workforce-transition.md | 1.5-2M workers affected, union considerations, retraining, SATS case study |
90-synthesis/decisions/decision-framework.md | Architectural decision framework and diffusion planning guide |
30-autonomy-stack/vla-vlm/vlm-scene-understanding.md | VLM as co-pilot (not controller): DriveVLM CoT reasoning, DriveLM graph QA, NOTAM interpretation, turnaround status, FOD classification, anomaly detection, InternVL2-2B on Orin (300ms), $30-55K phased deployment |
60-safety-validation/verification-validation/airside-scenario-taxonomy.md | ISO 34502 adapted for airside ODD, Pegasus 6-layer model, 115 functional / 566 logical scenarios, SOTIF hazard catalog (H1-H8+), STPA control structure, risk matrix, testing strategy, regulatory coverage mapping |
70-operations-domains/airside/operations/ground-control-instructions.md | Airside instruction hierarchy (A-CDM→ATC→marshaller), A-SMGCS integration, D-TAXI digital clearance, NOTAM machine-readable parsing pipeline, marshaller gesture recognition (ViTPose+LSTM), NLU for ground control phraseology, instruction-to-trajectory mapping, conflict resolution priority, phased deployment $30-50K→$50-100K |
30-autonomy-stack/perception/overview/camera-fallback-perception.md | Camera-only degraded mode when LiDAR fails: Metric3D v2, DepthAnything v2 (15ms INT8 Orin), stereo depth (RAFT-Stereo, ZED 2i), BEVFormer-Tiny (35-50ms), confidence calibration, thermal stress, degraded mode architecture with speed reduction, Simplex integration |
70-operations-domains/deployment-playbooks/multi-airport-adaptation.md | Multi-airport scaling playbook: domain shift analysis, AMDB map bootstrapping (free FAA data saves 60-70% mapping cost), PointLoRA perception adaptation (500 labels), GNSS multipath mapping, seasonal adaptation, 8-week onboarding protocol, cost model ($75-150K per additional airport) |
40-runtime-systems/monitoring-observability/hmi-operator-interface.md | HMI design for airside AV: ISO 3691-4 operator interface, monitoring dashboard (ROS + Foxglove/web), trust calibration, 4-mode control architecture, handoff procedures (2-5s budget), operator training (40-80h), incident reporting → active learning, external crew communication (LED/audio), $5-15K per station |
30-autonomy-stack/localization-mapping/maps/semantic-mapping-learned-priors.md | Semantic maps + learned priors: Neural Map Prior (NMP, +5.4 mAP, +8.2 at night), PriorDrive unified prior encoding, T2SG topology scene graphs, conformal prediction for map uncertainty, fleet-based incremental map updates, 7-layer semantic map architecture, multi-airport LoRA adapters |
30-autonomy-stack/planning/safety-critical-planning-cbf.md | Formal safety for neural planners: CBF math framework (ECBF, HOCBF, stochastic/robust), neural CBF synthesis + conformal calibration (CP-NCBF), CBF-QP filter (<1ms on Orin), HJ reachability (DeepReach), game-theoretic planning (GameFormer level-K, GIME, Stackelberg), multi-agent CBFs (GCBF+ 1024 agents), CBF-Simplex three-layer architecture, airside-specific CBFs (aircraft proximity, jet blast, personnel, geofence, runway incursion) |
30-autonomy-stack/world-models/lidar-native-world-models.md | LiDAR-native world models: Copilot4D (65% Chamfer improvement), UnO (self-supervised occupancy), LidarDM (diffusion LiDAR generation), LiDARCrafter (language-guided 4D), 4D occupancy forecasting, point cloud prediction networks, AD-L-JEPA, self-supervised training for airside, Orin deployment (50-100ms), safety applications |
30-autonomy-stack/perception/overview/collaborative-fleet-perception.md | V2V cooperative perception: OPV2V/V2X-ViT/CoBEVT/CoBEVFlow SOTA, Where2comm bandwidth selection (95% perf at 1/64 bandwidth), HEAL heterogeneous agents, fleet occupancy map, collective FOD detection, 5G deployment, phased $15K→$115K |
30-autonomy-stack/planning/neuro-symbolic-scene-graphs.md | Neuro-symbolic reasoning: driving scene graphs, GNN interaction modeling (LaneGCN, HiVT, HDGT), knowledge graphs for traffic rules, STL-constrained planning (differentiable), compositional reasoning, LLM-symbolic hybrid, airport right-of-way encoding (9-level priority), NOTAM rule injection, interpretable decisions, certification argument structure |
60-safety-validation/verification-validation/testing-validation-methodology.md | AV testing methodology: V-model, ASAM OpenSCENARIO 2.0, N-wise covering arrays (1,280→40 tests), CMA-ES falsification, LLM scenario generation, metamorphic testing, SIL/HIL/VIL, Zhao-Weng formula (4,600 tests for 99.9% reliability), Bayesian safety, shadow mode criteria, regression CI/CD, digital twin, $105K first airport |
30-autonomy-stack/perception/overview/self-supervised-pretraining-driving.md | Unified SSL pre-training: contrastive (SLidR, ScaLR, PPKT), MAE (Voxel-MAE, GD-MAE, BEV-MAE), JEPA (AD-L-JEPA, V-JEPA 2), DINOv2 for driving, multi-modal pre-training (UniPAD, BEVDistill), LoRA fine-tuning, 50-80% label reduction, airside curriculum (road SSL→road supervised→airside SSL→airside supervised), $5-15K compute vs $80K+ labeling |
30-autonomy-stack/perception/overview/gaussian-splatting-driving.md | 3DGS for real-time perception/mapping: GaussianFormer (39.2 mIoU, 20 FPS, 3.2x less memory), GaussianFormer v2 (41.1 mIoU), GaussianOcc self-supervised (80% gap closure, zero labels), SplaTAM SLAM (<0.4cm ATE), MonoGS, LiDAR-Gaussian fusion, multi-LiDAR merging via covariance intersection, dynamic object tracking, semantic/panoptic Gaussians, LangSplat language grounding, FOD detection via map anomaly, aircraft proximity monitoring, hybrid PointPillars+GaussianFormer architecture, Orin ~92ms, $90K/12-18mo integration |
50-cloud-fleet/mlops/data-flywheel-airside.md | Closed-loop data flywheel: trigger-based collection (50GB/day/vehicle, 100% safety capture), auto-labeling (SAM+CLIP foundation models, 70-85% cost reduction to $1.50-3/frame), active learning (40-50% fewer labels, safety-weighted), continuous retraining (monthly cycle), shadow mode validation (1-2 weeks), A/B fleet testing, scenario mining (power-law long-tail), synthetic data ($23K for 35K frames), multi-airport LoRA ($2-8K/airport), mAP trajectory 45%→82% over 24mo, breakeven Month 18, $205K Year 1 |
60-safety-validation/runtime-assurance/runtime-verification-monitoring.md | Runtime verification: STL quantitative robustness as unified safety metric, 20 airside-specific STL specs (aircraft proximity, zone speed, geofence, runway incursion, jet blast), RTAMT tool for ROS, combined OOD detection (energy+Mahalanobis+ensemble, 95-98% AUROC), conformal prediction coverage guarantees, 9 airside OOD triggers, maximally permissive shields (1-5% intervention), Shield+CBF+Simplex three-layer defense-in-depth, safety MCU (STM32H725, $50-200/vehicle), METAR→ODD monitoring, WCET <5.5ms full suite, ISO 26262 ASIL decomposition, UL 4600 compliance, DO-178C formal methods credit, fleet-level anomaly correlation, $115-200K/32 weeks |
30-autonomy-stack/world-models/occupancy-flow-4d-scenes.md | Occupancy flow & 4D scene understanding: static→dynamic occupancy, scene flow (NSFP, ZeroFlow 0.028m EPE3D, DeFlow 0.023m SOTA), 4D forecasting (UnO self-supervised winner, OccSora diffusion, Cam4DOcc benchmark, SelfOccFlow), dynamic 3DGS (StreetGaussians, 4D-GS, K-Planes 10900x compression), flow-guided Frenet planning (60-70% collision reduction), temporal modeling (attention+GRU hybrid), sparse voxels (18x compression), Orin 26-40ms FP16 pipeline, class-agnostic motion prediction, $6-11K training cost |
30-autonomy-stack/perception/overview/streaming-temporal-perception.md | Streaming temporal perception: StreamPETR object-centric propagation (+6-8% NDS, <3ms overhead, implicit AMOTA 65.3%), Sparse4D v3 (71.9% NDS SOTA), multi-sweep LiDAR (3-sweep +2.5% mAP at +1.4ms), BEV temporal fusion (BEVFormer +10.1% NDS), latency-aware streaming (ASAP/LASP), temporal consistency filtering (eliminates de-icing/jet blast transients), extended airside track persistence (30s GSE, 300 frames aircraft), turnaround phase detection, video backbone comparison, $38K/13 weeks |
30-autonomy-stack/perception/overview/active-perception-sensor-scheduling.md | Active perception & sensor scheduling: context-aware model switching (35-45% compute savings), entropy-based attention allocation, foveated LiDAR (89% voxel reduction), multi-LiDAR scheduling (3-4 of 8 at full, 44% savings), early exit networks (48% average compute), risk-aware allocation, planner-guided attention, predictive load scheduling via A-CDM, safe model switching (3-frame overlap), 30-36% power savings for electric GSE, $25-40K/10 weeks |
60-safety-validation/verification-validation/formal-verification-neural-networks.md | Formal verification of neural networks: SMT (Reluplex, Marabou) and MILP for complete verification (<100K params), alpha-beta-CROWN over-approximation (VNN-COMP winner, millions of params), DeepPoly/PRIMA abstract interpretation, IBP/SABR certified training, Lipschitz bounds for safety margins, randomized smoothing, layered strategy (complete for policy/CBF/Simplex, scalable for PointPillars/CenterPoint, runtime for residual), auto_LiRPA code examples, ISO 3691-4/UL 4600/EU AI Act/EU Machinery Regulation compliance |
20-av-platform/compute/energy-efficient-inference-24-7.md | Energy-efficient 24/7 inference: Orin 15W/30W/50W power modes deep dive, dynamic model switching (40-60% low-complexity time), thermal management (-10C to +50C tarmac, throttling curves), battery-aware compute (SoC-correlated budgets), DLA offloading (5-10W concurrent), sleep/wake (<500ms wake-up), per-model watt profiling, fleet-level energy optimization, 8-15% more daily operating hours, 12-18C lower junction temperature, $15-25K implementation |
30-autonomy-stack/planning/reinforcement-learning-driving-policy.md | RL driving policy: CaRL (CoRL 2025 SOTA, PPO + route completion reward scales with batch size), IQL (best offline RL, consistent across traffic densities), SAC/TD3/TQC/CrossQ off-policy comparison, BC→offline RL→online RL three-phase pipeline, CQL conservative lower-bound Q-values, Decision Transformer (RL as sequence modeling), safe RL (CPO, Lagrangian PPO, CBF-QP filter decouples safety from performance), Recovery RL (emergency maneuvers), privileged-to-sensor distillation (comma.ai approach), DAgger with Frenet planner as oracle, RLPD 50/50 mixing for offline-to-online, policy head 0.5ms FP16 on Orin, Simplex integration (RL advanced + Frenet fallback), $45-75K over 32 weeks |
30-autonomy-stack/localization-mapping/maps/hd-map-change-detection-maintenance.md | HD map change detection and maintenance: point cloud differencing (ICP-based, KD-tree), semantic change detection (class-based filtering), RTMap (ICCV 2025, centimeter-level recursive map maintenance), Bayesian fleet consensus (per-vehicle reliability, posterior >0.99 for safety-critical), DBSCAN spatial clustering, temporal decay model (feature-type half-lives: structures 365d, barriers 30d, equipment 7d), AIRAC 28-day cycle integration (dual-layer: regulatory AIRAC + operational fleet), light-map alternative (720 KB topology+safety+regulatory), NMP implicit maintenance, 3DGS map updates (opacity decay), OTA canary deployment (10% fleet first, 2h monitoring), construction zone + NOTAM corroboration, cost: $45-70K/28 weeks, 60-80% reduction vs manual re-survey, break-even at 2-3 airports |
70-operations-domains/airside/business-case/fleet-tco-business-case.md | Fleet TCO and business case: per-vehicle CAPEX ($95-210K floor at scale), LiDAR-only sensor kit $29-60K, full suite $47-84K, vehicle integration $20-30K, 3-shift labor savings $150K/year per position, accident avoidance $150-750K/year for 20 vehicles, scale dynamics (pilot $400-650K/vehicle → mature $155-330K), multi-airport marginal cost $600K→$115K, certification $530K-1.95M across 5 jurisdictions, operator ratio 1:5→1:10+ as key OPEX lever, break-even Year 2-4, 10-year NPV $45-80M at 200 vehicles (8% discount), RaaS $10-14K/month, probability-weighted expected NPV ~$25M, regulatory delay -$8-15M/year NPV impact, UISEE 40-60% cost advantage, airport cluster deployment strategy, $2-6B TAM at 10% penetration |
30-autonomy-stack/multi-agent-v2x/v2x-protocols-airside.md | V2X communication protocols for airside: C-V2X over private 5G/CBRS (preferred, sub-ms URLLC), DSRC comparison, ETSI ITS message architecture (CAM 1-10 Hz, DENM events, CPM perception sharing, MCM maneuver coordination), 8 custom airside messages (Aircraft Proximity Alert, Stand Operation Status, GSE Task Assignment, De-Icing Zone, Emergency Vehicle Priority, Runway Incursion Prevention default-deny model, FOD Detection Alert, Jet Blast Warning — highest criticality invisible hazard), protobuf field-level specs with example payloads, A-CDM/A-SMGCS/ADS-B/AODB bridge architecture, bandwidth planning (123 Mbps for 50 vehicles, zone filtering needed at 200+), PKI with airport-managed CA hierarchy, misbehavior detection trust scoring, fallback safe behavior without V2X (5 km/h + 2x margins), cooperative perception +15-25% AP, standards predicted 2028-2030, $270-450K full implementation, V2X hardware $200-600/vehicle on existing 5G |
Document Statistics
| Metric | Value |
|---|---|
| Reader Markdown pages | 601 |
| Core research documents | 597 |
| Reader/research lines | 334k+ |
00-start-here/ documents | 4 |
10-knowledge-base/ documents | 99 |
20-av-platform/ documents | 29 |
30-autonomy-stack/ documents | 315 |
40-runtime-systems/ documents | 10 |
50-cloud-fleet/ documents | 21 |
60-safety-validation/ documents | 33 |
70-operations-domains/ documents | 24 |
80-industry-intel/ documents | 52 |
90-synthesis/ documents | 10 |
| Companies covered | 20 |
| Technology domains | 9 |
| Method-level SLAM library | 100 method files + overview/audit |
| Method-level perception files | 93 |
| Safety and validation documents | 33 |
| AV platform documents | 29 |
| Knowledge base documents | 99 |
| Synthesis documents | 10 |
| Perception documents | 141 |
| Localization/mapping | 116 |
| Planning documents | 15 |
| Multi-agent and V2X | 6 |
| Robustness validation files | 4 |
| Papers referenced | 700+ |
| Open-source repos evaluated | 90+ |
| Occupancy methods compared | 20 |
| Online mapping methods compared | 16 |
| Cooperative perception methods | 10+ |
| Airport deployments documented | 15+ |