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	601
Core research documents	597
Corpus size	334k+ lines
Companies covered	20
Technology domains	9
Method-level SLAM library	100 method files + overview/audit
Method-level perception files	93
Safety and validation docs	33
AV platform docs	29
Synthesis docs	10
Knowledge base docs	99
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.
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, occupancy, LiDAR-camera/radar-camera fusion, dynamic Gaussian/3DGS/4DGS, LiDAR MOS, scene flow, 4D radar, FMCW LiDAR, open-world occupancy/attributes, robust fusion, V2X, latency, and data-engine methods into single-technique research pages.
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, STATIC-LIO, MOVES, RTMap/DUFOMap, LT-mapper/Khronos, 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-free driving, map maintenance, 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, LIVO, visual, dense, neural, Gaussian, radar, and multi-sensor SLAM into focused method files.
SLAM coverage gaps	SLAM Coverage Audit	Tracks missing first-class SLAM pages, including May 2026 sweeps across LIO, LIVO, 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, and sparse backend choices, with links back into probability, optimization, numerical linear algebra, 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, 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, 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, STU 3D anomaly segmentation, RCP-Bench, V2X datasets, sensor-corruption benchmarks, open-world/OOD anomaly segmentation, FOD datasets, synthetic FOD validation, FOD validation, 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/	125	Probability and Statistics Foundations	First-principles technical notes: probability/statistics, optimization, numerical linear algebra, geometry, mapping, state estimation, sensor likelihoods, signal processing, controls, robotics, ML, calibration, timing, continuous-time trajectories, and detection/tracking evidence.
20-av-platform/	29	NVIDIA Orin Technical	Compute, sensors, sensor-to-algorithm readiness, connectivity, drive-by-wire, power, diagnostics, ruggedization, and edge-cloud architecture.
30-autonomy-stack/	315	World Models Overview	World models, perception, method-level perception, planning, localization, SLAM, simulation, VLA/VLM, E2E driving, and multi-agent systems.
40-runtime-systems/	10	Production ML Deployment	ML deployment, ROS/Autoware, observability, teleoperation, software operations, and vehicle-side data logging.
50-cloud-fleet/	21	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/	33	Certification Guide	Safety case, standards, runtime assurance, verification, validation, robustness, cybersecurity, incident reporting, reliability evidence, and evidence traceability.
70-operations-domains/	24	Airside Industry Overview	Airside, warehouse, yard, port, mining, agriculture, construction, road AV, delivery robot, deployment, business-case, and safety operations.
80-industry-intel/	52	Company Index	AV, airside, simulation, teleoperation, autonomy company profiles, market intelligence, and regulations.
90-synthesis/	10	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	141
Method-level perception library	93
Planning	15
Localization and mapping	116
Method-level SLAM library	100 method files + overview/audit
Simulation	7
VLA / VLM	6
Multi-agent and V2X	6
Robustness validation files	5
E2E driving	6

Operations	Docs
Safety and validation	33
Deployment	13
Airside operations	10
Cross-domain operations	9
Teleoperation	1

AV Platform	Docs
Compute	7
Sensors	14
Networking/connectivity	3
Drive-by-wire	2
Power/electrical	1
Diagnostics	1
Ruggedization	1

Reader Notes

• The static reader is generated from this repository with VitePress and deployed through GitHub Pages.
• README.md becomes the site home page.
• INDEX.md is 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.