# Nexar Dashcam Collision Prediction Dataset and Challenge

arXiv: https://arxiv.org/abs/2503.03848 · HTML: https://mlsanity.com/paper/2503.03848/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling](https://mlsanity.com/paper/2501.12386/) (2025)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://mlsanity.com/paper/1903.00618/) (2019)
- [DADA: Driver Attention Prediction in Driving Accident Scenarios](https://mlsanity.com/paper/1912.12148/) (2019)
- [Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB](https://mlsanity.com/paper/1804.02675/) (2018)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [Abductive Ego-View Accident Video Understanding for Safe Driving Perception](https://mlsanity.com/paper/2403.00436/) (2024)
- [A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents](https://mlsanity.com/paper/2106.10197/) (2021)
- [Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization](https://mlsanity.com/paper/1705.06560/) (2017)
- [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1912.07773/) (2019)
- [Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction](https://mlsanity.com/paper/2111.06123/) (2021)
- [When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos](https://mlsanity.com/paper/2004.03044/) (2020)
- [VIENA2: A Driving Anticipation Dataset](https://mlsanity.com/paper/1810.09044/) (2018)
- [Global Feature Aggregation for Accident Anticipation](https://mlsanity.com/paper/2006.08942/) (2020)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions](https://mlsanity.com/paper/2407.17757/) (2024)
- [Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling](https://mlsanity.com/paper/2409.01256/) (2024)
- [Drive Video Analysis for the Detection of Traffic Near-Miss Incidents](https://mlsanity.com/paper/1804.02555/) (2018)

## Built on by (most cited first, 21 of 21)

- [BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data](https://mlsanity.com/paper/2510.14876/) (2025)
- [Accident Anticipation via Temporal Occurrence Prediction](https://mlsanity.com/paper/2510.22260/) (2025)
- [iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning](https://mlsanity.com/paper/2509.19552/) (2025)
- [GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents](https://mlsanity.com/paper/2603.24329/) (2026)
- [PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning](https://mlsanity.com/paper/2606.02443/) (2026)
- [Understanding Real-World Traffic Safety through RoadSafe365 Benchmark](https://mlsanity.com/paper/2602.07212/) (2026)
- [Artifacts of Idiosyncracy in Global Street View Data](https://mlsanity.com/paper/2505.11046/) (2025)
- [2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving](https://mlsanity.com/paper/2508.21080/) (2025)
- [CrashChat: A Multimodal Large Language Model for Multitask Traffic Crash Video Analysis](https://mlsanity.com/paper/2512.18878/) (2025)
- [Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments](https://mlsanity.com/paper/2603.15916/) (2026)
- [VAGNet: Vision-based Accident Anticipation with Global Features](https://mlsanity.com/paper/2604.09305/) (2026)
- [AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models](https://mlsanity.com/paper/2604.20878/) (2026)
- [StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement](https://mlsanity.com/paper/2606.00267/) (2026)
- [Driving Video Retrieval for Complex Queries with Structured Grounding](https://mlsanity.com/paper/2606.09109/) (2026)
- [SAVeD: A First-Person Social Media Video Dataset for ADAS-equipped vehicle Near-Miss and Crash Event Analyses](https://mlsanity.com/paper/2512.17724/) (2025)
- [Enhancing Multimodal Large Language Models for Safety-Critical Driving Video Analysis](https://mlsanity.com/paper/2605.22185/) (2026)
- [VZCrash: A Large-Scale IMU Dataset of Ego-Vehicle Crashes](https://mlsanity.com/paper/2606.06074/) (2026)
- [FLaRA: Predicting Future Latent Representations for Accident Anticipation](https://mlsanity.com/paper/2606.14380/) (2026)
- [CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios](https://mlsanity.com/paper/2608.19380/) (2026)
- [Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models](https://mlsanity.com/paper/2609.08130/) (2026)
- [Progressive Risk Estimation for Accident Anticipation](https://mlsanity.com/paper/2609.32811/) (2026)

## Abstract (arXiv)

This paper presents the Nexar Dashcam Collision Prediction Dataset and Challenge, designed to support research in traffic event analysis, collision prediction, and autonomous vehicle safety. The dataset consists of 1,500 annotated video clips, each approximately 40 seconds long, capturing a diverse range of real-world traffic scenarios. Videos are labeled with event type (collision/near-collision vs. normal driving), environmental conditions (lighting conditions and weather), and scene type (urban, rural, highway, etc.). For collision and near-collision cases, additional temporal labels are provided, including the precise moment of the event and the alert time, marking when the collision first becomes predictable. To advance research on accident prediction, we introduce the Nexar Dashcam Collision Prediction Challenge, a public competition on top of this dataset. Participants are tasked with developing machine learning models that predict the likelihood of an imminent collision, given an input video. Model performance is evaluated using the average precision (AP) computed across multiple intervals before the accident (i.e. 500 ms, 1000 ms, and 1500 ms prior to the event), emphasizing the importance of early and reliable predictions. The dataset is released under an open license with restrictions on unethical use, ensuring responsible research and innovation.