Nexar Dashcam Collision Prediction Dataset and Challenge

2025-03-05

AI Summary

This work addresses the early prediction of imminent collisions in real-world traffic scenarios. We introduce the first fine-grained collision prediction dataset comprising 1,500 real 40-second videos, annotated with collision event types, environmental and scene attributes, precise event timestamps, and—novelly—the “predictable moment,” i.e., the earliest time at which a model can reliably issue a warning. To holistically evaluate timeliness and reliability, we propose a joint average precision (AP) metric across multiple lead times (500 ms, 1,000 ms, and 1,500 ms). Our method integrates video understanding, temporal action detection, and multi-task event forecasting. The dataset is publicly released under ethical usage constraints. Benchmark models achieve significant AP improvements at the 1,500-ms lead time, advancing research on high-temporal-precision, deployable safety warning systems for autonomous driving.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Develops a dataset for traffic event analysis and collision prediction.
• Introduces a challenge to predict imminent collisions using video data.
• Evaluates models on early and reliable collision prediction accuracy.

Innovation

• Annotated video clips for collision prediction
• Machine learning models for imminent collision prediction
• Open dataset with ethical use restrictions