Industry Research
Markdown-first knowledge base for autonomous vehicle technology across road, airside, warehouse, logistics yard, port, mining, construction, agriculture, delivery robot, and outdoor campus domains. Airside autonomous vehicles remain the best-developed reference ODD, not the default evaluation lens.
Read it as a site: https://kvynlim.github.io/industry-research/
The repository remains Markdown-first, but the VitePress reader is the intended reading surface: local search, generated sidebar navigation, clean URLs, last-updated metadata, and source links back into the repo.
Current Shape
| Scope | Count |
|---|---|
| Reader pages | 870 |
| Core research documents | 866 |
| Corpus size | 390k+ lines |
| Companies covered | 25 |
| Technology domains | 9 |
| Method-level SLAM library | 158 SLAM-method documents including overview/audit |
| Method-level perception files | 138 |
| Safety and validation docs | 56 |
| AV platform docs | 41 |
| Synthesis docs | 12 |
| Knowledge base docs | 137 |
| Papers referenced | 700+ |
| Open-source repos evaluated | 90+ |
| Airport deployments documented | 15+ |
Architecture
The corpus is being organized as an end-to-end AV knowledge base: fundamentals, platform hardware, autonomy stack, runtime systems, cloud/fleet systems, safety validation, operations domains, industry intelligence, and synthesis.
Airside is used as a detailed reference ODD where the corpus has the deepest deployment evidence. Generic autonomy-stack methods, ratings, and synthesis pages should still state how ideas transfer across road AVs, warehouses, yards, ports, mines, construction sites, farms, delivery robots, and campus systems.
Start Here
| Need | Open |
|---|---|
| Navigate the whole corpus | Research Index |
| Get the executive view | Master Synthesis |
| Start building from the research | Getting Started |
| Pick concrete POCs | POC Proposals |
| Understand readiness and risk | Technology Readiness |
| Prioritize gap-filling research | Knowledge Gap Backlog |
| Continue the research loop | Continuous Research Loop |
| Monitor active research sources | Active Frontier Source Registry |
| Compare the market | Competitive Landscape |
| Read the core system architecture | Design Spec |
| Go deep on perception methods | Method-Level Perception Library |
| Go deep on SLAM methods | Method-Level SLAM Library |
| Check terms and abbreviations | Glossary |
| Understand how the corpus was made | Methodology |
High-Leverage Reading Paths
| Path | Best Entry Point | Why |
|---|---|---|
| World models for autonomous driving | World Models Overview | Frames diffusion, occupancy, self-supervised occupancy flow, UniScene-style occupancy-centric generation, tokenized, JEPA, RL, and LiDAR-native approaches. |
| Airport airside operations | Airside Industry Overview | Connects the AV stack to pushback, turnaround, FOD, jet blast, airport data systems, and GSE. |
| Cross-domain deployment signals | 2024-2026 Autonomy Deployment Index | Compares airside, yard, warehouse, mining, delivery, and road ADS deployment evidence without treating one ODD as the default. |
| Safety case and certification | Certification Guide | Pulls together ISO 3691-4, UL 4600, SOTIF, runtime monitoring, fail-operational design, and validation. |
| Production deployment | Deployment Playbook | Turns research into staged rollout, shadow mode, OTA, fleet management, and operational procedures. |
| MLOps by scale | MLOps Scale Research Scope, MLOps Reference Architectures by Scale, MLOps Migration Checklist, MLOps Scorecards and KPIs by Scale, Data Catalog, Lineage, and Quality Ops, Experiment Tracking and Reproducibility, Model Registry and Artifact Lifecycle, Serving and Inference Operations, MLOps Platform SRE and Reliability, Pipeline Orchestration and Release Workflows, Evaluation Platforms and Replay Gates, Dataset Split and Leakage Controls, Model Monitoring and Drift Response, Site-Sliced Release Evidence, Feature and Embedding Store Operations, Offboard Labeler Registry, GPU Queueing and FinOps, Secure Artifact Attestation, Federated and Privacy-Preserving Training Policy, LLMOps and Agent Evaluation, and Map-Derived Pseudo-Label Invalidation | Routes MLOps from notebook research through repeatable prototypes, production products, fleet-scale autonomy, regulated safety-critical release, and foundation-model/platform scale, covering data-product contracts, catalog/lineage quality gates, run authority and reproducibility levels, registry identity and alias authority, serving/inference manifests, endpoint and batch-serving architecture, platform SRE/reliability, workflow state machines, evaluation manifests, replay/runtime gates, shadow/canary evidence, split/leakage firewalls, monitoring/drift response, label operations, registries, orchestration, governance, autonomy-specific map/model/calibration evidence, centralization boundaries, concrete S0-S5 architecture patterns, migration gates, release blockers, scorecards, ODD-cell release evidence, feature/vector-store controls, offboard labeler governance, GPU queueing/FinOps, secure artifact signing/SBOM/provenance/registry verification, federated/hybrid/privacy-preserving training triggers, GenAIOps/LLMOps agent evaluation, and pseudo-label invalidation. |
| Fleet economics | Fleet TCO Business Case | Tracks vehicle CAPEX, labor savings, certification costs, operator ratios, and break-even logic. |
| Edge hardware choices | NVIDIA Orin Technical | Grounds model choices in compute, power, TensorRT, DLA, and sensor constraints. |
| Perception stack | Production Perception Systems | Compares production AV approaches and the perception patterns that transfer across road, airside, and managed-site autonomy. |
| Method-level perception | Perception Method Library | Splits BEV, sparse-query detection, occupancy, GaussianFlowOcc/GaussTR/GS-Occ3D/VOGS-CP-style Gaussian occupancy and label curation, LiDAR-camera/radar-camera fusion, dynamic Gaussian/3DGS/4DGS, point-cloud Mamba/SSM backbones, LOSC-style LiDAR pseudo-label consolidation, LiDAR MOS, scene flow, 4D radar, FMCW LiDAR, open-world occupancy/attributes including SpaCeFormer-style open-vocabulary 3D instance segmentation, robust fusion, QuantV2X/SparseCoop-style V2X compression and sparse-query cooperation, latency, and data-engine methods into single-technique research pages. |
| Aggregated-map semantic segmentation | Aggregated-Map Semantic Segmentation | Routes the full registered-map labeling pipeline: LiDAR/image inputs and modality release contracts, ML-related SLAM substrate scope, application architecture patterns for runtime maps/training exports/monitoring/digital twins/benchmarks, first-principles map-state/evaluation grounding, georeferenced source-map conditioning via OpenLiDARMap/FlexCloud-style provenance gates, source-map acceptance packages, LAMM/Uni-Mapper-style multi-session map merging plus MapEval-style source-map geometry QA before segmentation, release-oriented dataset selection including non-road urban-district benchmark bundles, GridNet-HD managed-site LiDAR-image transfer, and Point Cloud City / City-Facade / ZAHA managed-building and facade proxies, class-taxonomy design with a semantic-vs-permanence layer split, release-state-aware training objectives and map-derived training export masks that require semantic class plus release-state labels, modality-aware backbone/training/deployment-contract tradeoffs, schema-backed semantic-map/runtime contracts with map-hygiene layer digests and metrics, compact proxy/input/training selector, point-cloud SSM efficiency frontiers, LOSC-style pseudo-label consolidation, training architectures, tiling/stitching with tile release ledgers and release-state seam checks, post-processing with semantic/confidence/hygiene layer outputs and release-state-preserving smoothing, map cleaning handoff, map-hygiene ground-truth workflow, and non-road urban-district transfer. |
| ML-related SLAM for semantic maps | ML-Related SLAM Research Scope | Connects learned registration, learned place recognition, semantic/dynamic SLAM, neural implicit and Gaussian SLAM, point-cloud removal, static-but-transient quarantine, layered removal labels, multi-session map merging, learned map priors, and aggregated-map semantic segmentation into one research architecture with an explicit handoff contract for what may steer publication versus QA or training. |
| LiDAR artifact removal | LiDAR Artifact Removal Techniques | Connects LIORNet, learned denoisers, classical outlier filters, weather artifacts, ghost/multipath behavior, map cleaning, datasets, and safety validation. |
| Dynamic and static object removal | LiDAR Map Cleaning and Dynamic Removal | Connects ERASOR, Removert, MapCleaner, ERASOR++, 4dNDF, FreeDOM, BeautyMap, Raymoval, STATIC-LIO, MOVES, detector-based potentially dynamic object removal, dynamic residual cleanup, stationary-person hard exclusion, static-but-transient quarantine, reason-coded release decisions and removal sidecars, release-state benchmark labels, static-but-wrong map exclusion, Uni-Mapper dynamic-aware heterogeneous-LiDAR map merging, LAMM large-scale multi-session point-cloud map merging, MapEval post-cleaning geometry QA, RTMap/DUFOMap, LT-mapper/Khronos, lifelong map version control, MOS/scene-flow methods, moved-object datasets, and false-deletion validation. |
| Perception coverage gaps | Perception Coverage Audit | Tracks missing first-class perception pages across BEV, occupancy, Gaussian/3DGS, LiDAR/radar/thermal, open-world/OOD, V2X, robustness, and benchmarks. |
| Localization and mapping | Mapping and Localization | Covers HD maps, LiDAR SLAM, map-prior georeferenced construction, multi-session point-cloud map merging, point-cloud map-quality evaluation, map-free driving, map maintenance, infrastructure-aided localization, and occupancy grids. |
| Photoreal city-scale 4D reconstruction | Photoreal city-scale 4D reconstruction | Links Gaussian SLAM, VGGT/feed-forward reconstruction, dynamic 4D Gaussian/NeRF methods, and digital-twin simulation coverage. |
| Method-level 3D SLAM | SLAM Library Overview | Breaks classical, LiDAR including RKO-LIO sensor-agnostic LIO, LIVO, visual, dense, neural, Gaussian, radar, learned 4D radar odometry, radar place-recognition descriptors, radar RIO correspondence/uncertainty, Doppler radar-LiDAR bridge SLAM, raw GNSS factor fusion, wheel/vehicle-motion factors, radar-GNSS/visual mapping, multi-sensor, SLAM Toolbox, NDT variants, static-map lifecycle/removal, heterogeneous map-merging, LAMM multi-session point-cloud map merging, MapEval map-quality evaluation, and current SLAM benchmark coverage into focused method files. |
| GLIM and GTSAM pipeline | GLIM and GTSAM Pipeline Hub | Maps GLIM pipeline stages to GTSAM objects, Bayes-tree updates, Hessians, sparse elimination, marginalization, and the supporting KB math pages. |
| SLAM coverage gaps | SLAM Coverage Audit | Tracks promoted and remaining first-class SLAM pages, including May 2026 sweeps across LIO, LIVO, raw GNSS factor fusion, wheel/vehicle-motion factors, 4D radar, Gaussian/foundation SLAM, backends, collaborative SLAM, alternative sensors, and benchmarks. |
| First-principles estimator math | Nonlinear Solver Diagnostics Crosswalk | Routes estimator failures across residuals, Jacobians, scaling, damping, rank, covariance, constrained KKT/QP/SQP mechanics, robust-loss covariance consistency, fiducial/corner localization, two-view epipolar/homography verification, optical/scene-flow motion fields, and sparse backend choices, with links back into probability, optimization, numerical linear algebra, geometry, and state estimation foundations. |
| Machine learning foundations | Machine Learning Foundations | Starts from linear models and gradients through CNN/RNN/transformer/SSM foundations, self-supervision, world models, AV data-evaluation contracts, calibration, evaluation, and deployment review. |
| Control and decision foundations | Control Foundations | Starts the foundations path for closed-loop tracking, vehicle dynamics, MPC/iLQR, constraints, MDP/POMDP decision models, safety filters, and planner-controller review. |
| Sensor and estimation fundamentals | Sensor Foundations | Starts the sensor-model foundation path, with supporting links into geometry, thermal IR radiometry, ultrasonic proximity sensing, state estimation, signal processing, timing, calibration, and wheel odometry. |
| Sensor readiness before algorithms | Sensor-to-Algorithm Readiness Contract | Consolidates calibration, synchronization, preprocessing, health, provenance, and fail-closed gates before perception, fusion, SLAM, tracking, occupancy, mapping, or planning consumes sensor-derived inputs. |
| Perception validation datasets | FOD and Airport Apron Detection Datasets | Connects MUSES, DSERT-RoLL, CMHT, EmbodiedScan/MMScan, GridNet-HD, non-road district benchmark bundles, STU 3D anomaly segmentation, RCP-Bench, TruckV2X, V2X datasets, sensor-corruption benchmarks, open-world/OOD anomaly segmentation, FOD datasets, Airport-FOD3S synthetic data-engine routing, synthetic FOD validation, FOD validation, public-proxy boundary caveats, and knowledge-base evaluation protocols. |
| End-to-end architecture gaps | Knowledge Gap Backlog | Tracks P0/P1/P2 missing research files across fundamentals, platform, autonomy, runtime/cloud, safety, operations, and industry intelligence. |
Corpus Map
| Section | Docs | Start At | What It Holds |
|---|---|---|---|
00-start-here/ | 4 | Reading Guide | Reader entry points and orientation material. |
10-knowledge-base/ | 137 | Probability and Statistics Foundations | First-principles technical notes: probability/statistics, optimization, constrained KKT/QP/SQP solver mechanics, numerical linear algebra, geometry, mapping, state estimation, sensor likelihoods, thermal IR radiometry, ultrasonic proximity sensing, signal processing, controls, robotics, ML including AV data-evaluation contracts, calibration, timing, continuous-time trajectories, robust covariance consistency, fiducial/corner localization, two-view epipolar/homography verification, optical/scene-flow motion fields, and detection/tracking evidence. |
20-av-platform/ | 41 | NVIDIA Orin Technical | Compute, sensors, sensor-to-algorithm readiness, connectivity, drive-by-wire, power, diagnostics, ruggedization, and edge-cloud architecture. |
30-autonomy-stack/ | 459 | World Models Overview | World models, perception, method-level perception, planning, localization, infrastructure-aided localization, SLAM, simulation, VLA/VLM, E2E driving, and multi-agent systems. |
40-runtime-systems/ | 23 | Production ML Deployment | ML deployment, ROS/Autoware, observability, teleoperation, software operations, and vehicle-side data logging. |
50-cloud-fleet/ | 52 | Cloud Backend Infrastructure | Data engines, fleet data loops, MLOps, OTA/SUMS, observability, map operations, data governance, perception/SLAM reliability telemetry, and fleet management. |
60-safety-validation/ | 56 | Certification Guide | Safety case, standards, runtime assurance, verification, validation, robustness, cybersecurity, incident reporting, reliability evidence, and evidence traceability. |
70-operations-domains/ | 27 | Airside Industry Overview | Airside, warehouse, yard, port, mining, agriculture, construction, road AV, delivery robot, deployment, business-case, and safety operations. |
80-industry-intel/ | 62 | Company Index | AV, airside, simulation, teleoperation, autonomy company profiles, market intelligence, and regulations. |
90-synthesis/ | 12 | Master Synthesis | Executive synthesis, POCs, readiness, risk, decision framework, architecture, gap backlog, continuous research loop, and active frontier source registry. |
Domain Snapshot
| Technology | Docs |
|---|---|
| World models | 18 |
| Perception | 212 |
| Method-level perception library | 138 |
| Planning | 16 |
| Localization and mapping | 185 |
| Method-level SLAM library | 158 SLAM-method documents including overview/audit |
| Simulation | 8 |
| VLA / VLM | 7 |
| Multi-agent and V2X | 7 |
| Robustness validation files | 9 |
| E2E driving | 6 |
| Operations | Docs |
|---|---|
| Safety and validation | 56 |
| Deployment | 13 |
| Airside operations | 11 |
| Cross-domain operations | 9 |
| Teleoperation | 1 |
| AV Platform | Docs |
|---|---|
| Compute | 9 |
| Sensors | 19 |
| Networking/connectivity | 6 |
| Drive-by-wire | 2 |
| Power/electrical | 1 |
| Diagnostics | 2 |
| Ruggedization | 1 |
| Thermal | 1 |
Reader Notes
- The static reader is generated from this repository with VitePress and deployed through GitHub Pages.
README.mdbecomes the site home page.INDEX.mdis served as/INDEX/in the reader to avoid a Windows case-insensitive output collision with the homepage.- Research content is source-of-truth Markdown; the generated site is just a browser-friendly layer over the same files.
- Internal implementation notes under
docs/superpowers/,.claude/, and.superpowers/are excluded from the static reader.