# Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps

arXiv: https://arxiv.org/abs/2501.16480 · HTML: https://mlsanity.com/paper/2501.16480/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 27 Jan 2025.

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

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion](https://mlsanity.com/paper/2203.13777/) (2022)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [GenAD: Generalized Predictive Model for Autonomous Driving](https://mlsanity.com/paper/2403.09630/) (2024)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [Deep Learning for Vision-based Prediction: A Survey](https://mlsanity.com/paper/2007.00095/) (2020)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction](https://mlsanity.com/paper/2308.01707/) (2023)
- [Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving](https://mlsanity.com/paper/2407.21126/) (2024)
- [Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture](https://mlsanity.com/paper/2404.03789/) (2024)
- [Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning](https://mlsanity.com/paper/1806.07635/) (2018)
- [Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability](https://mlsanity.com/paper/2210.02131/) (2022)
- [Procedure for the Safety Assessment of an Autonomous Vehicle Using Real-World Scenarios](https://mlsanity.com/paper/2012.00643/) (2020)
- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)

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

- [Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment](https://mlsanity.com/paper/2607.11128/) (2026)

## Abstract (arXiv)

Accurately assessing collision risk in dynamic traffic scenarios is a crucial requirement for trajectory planning in autonomous vehicles~(AVs) and enables a comprehensive safety evaluation of automated driving systems. To that end, this paper presents a novel probabilistic occupancy risk assessment~(PORA) metric. It uses spatiotemporal heatmaps as probabilistic occupancy predictions of surrounding traffic participants and estimates the risk of a collision along an AV's planned trajectory based on potential vehicle interactions. The use of probabilistic occupancy allows PORA to account for the uncertainty in future trajectories and velocities of traffic participants in the risk estimates. The risk from potential vehicle interactions is then further adjusted through a Cox modeledit, which considers the relative editmotion between the AV and surrounding traffic participants. We demonstrate that the proposed approach enhances the accuracy of collision risk assessment in dynamic traffic scenarios, resulting in safer vehicle controllers, and provides a robust framework for real-time decision-making in autonomous driving systems. From evaluation in Monte Carlo simulations, PORA is shown to be more effective at accurately characterizing collision risk compared to other safety surrogate measures. Keywords: Dynamic Risk Assessment, Autonomous Vehicle, Probabilistic Occupancy, Driving Safety