# Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors

arXiv: https://arxiv.org/abs/2502.00402 · HTML: https://mlsanity.com/paper/2502.00402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 28 of 28)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research](https://mlsanity.com/paper/2204.06527/) (2022)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles](https://mlsanity.com/paper/2402.07635/) (2024)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [IPS300+: a Challenging Multimodal Dataset for Intersection Perception System](https://mlsanity.com/paper/2106.02781/) (2021)
- [InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors](https://mlsanity.com/paper/2305.00314/) (2023)
- [Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets](https://mlsanity.com/paper/2402.07320/) (2024)
- [TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance](https://mlsanity.com/paper/2209.12386/) (2022)
- [The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration](https://mlsanity.com/paper/2401.16634/) (2024)
- [ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2402.03235/) (2024)
- [TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras](https://mlsanity.com/paper/2401.08474/) (2024)
- [Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction](https://mlsanity.com/paper/2301.05805/) (2023)
- [A Survey of Robust 3D Object Detection Methods in Point Clouds](https://mlsanity.com/paper/2204.00106/) (2022)
- [Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation](https://mlsanity.com/paper/2204.00132/) (2022)
- [Real-Time And Robust 3D Object Detection with Roadside LiDARs](https://mlsanity.com/paper/2207.05200/) (2022)
- [Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis](https://mlsanity.com/paper/2305.04506/) (2023)
- [WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection](https://mlsanity.com/paper/2407.20818/) (2024)
- [Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection](https://mlsanity.com/paper/2310.10038/) (2023)
- [Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models](https://mlsanity.com/paper/2210.14584/) (2022)
- [GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs](https://mlsanity.com/paper/2405.06782/) (2024)
- [PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems](https://mlsanity.com/paper/2405.01750/) (2024)
- [Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection](https://mlsanity.com/paper/2408.15637/) (2024)

## Built on by (most cited first, 1 of 1)

- [LangCoop: Collaborative Driving with Language](https://mlsanity.com/paper/2504.13406/) (2025)

## Abstract (arXiv)

Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address human errors like speeding, drunk driving, and distractions. Additionally, faster accident detection and quicker medical response can help save lives. We propose an accident detection framework that combines a rule-based approach with a learning-based one. We introduce a dataset of real-world highway accidents featuring high-speed crash sequences. It includes 294,924 labeled 2D boxes, 93,012 labeled 3D boxes, and track IDs across 48,144 frames captured at 10 Hz using four roadside cameras and LiDAR sensors. The dataset covers ten object classes and is released in the OpenLABEL format. Our experiments and analysis demonstrate the reliability of our method.