# Learning collision risk proactively from naturalistic driving data at scale

arXiv: https://arxiv.org/abs/2505.13556 · HTML: https://mlsanity.com/paper/2505.13556/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 83 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Orthogonal Random Features](https://mlsanity.com/paper/1610.09072/) (2016)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [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)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Drive Video Analysis for the Detection of Traffic Near-Miss Incidents](https://mlsanity.com/paper/1804.02555/) (2018)
- [Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning](https://mlsanity.com/paper/2209.15133/) (2022)
- [A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution](https://mlsanity.com/paper/2410.06662/) (2024)
- [Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving](https://mlsanity.com/paper/2303.07181/) (2023)
- [A Unified Probabilistic Approach to Traffic Conflict Detection](https://mlsanity.com/paper/2407.10959/) (2024)
- [PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation](https://mlsanity.com/paper/2303.07891/) (2023)

## Built on by (most cited first, 3 of 3)

- [Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment](https://mlsanity.com/paper/2607.11128/) (2026)
- [Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models](https://mlsanity.com/paper/2609.08130/) (2026)
- [Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation](https://mlsanity.com/paper/2610.04366/) (2026)

## Abstract (arXiv)

Accurately and proactively alerting drivers or automated systems to emerging collisions is crucial for road safety, particularly in highly interactive and complex urban environments. Existing methods either require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are tailored to limited scenarios. Here we present the Generalised Surrogate Safety Measure (GSSM), a data-driven approach that learns collision risk from naturalistic driving without the need for crash or risk labels. Trained over multiple datasets and evaluated on 2,591 real-world crashes and near-crashes, a basic GSSM using only instantaneous motion kinematics achieves an area under the precision-recall curve of 0.9, and secures a median time advance of 2.6 seconds to prevent potential collisions. Incorporating additional interaction patterns and contextual factors provides further performance gains. Across interaction scenarios such as rear-end, merging, and turning, GSSM consistently outperforms existing baselines in accuracy and timeliness. These results establish GSSM as a scalable, context-aware, and generalisable foundation to identify risky interactions before they become unavoidable, supporting proactive safety in autonomous driving systems and traffic incident management. Code and experiment data are openly accessible at https://github.com/Yiru-Jiao/GSSM.