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

TL;DR

This paper introduces a probabilistic occupancy risk assessment method for autonomous vehicles that uses spatiotemporal heatmaps to improve collision risk estimation and enhance safety in dynamic traffic scenarios.

Contribution

It presents a novel PORA metric utilizing probabilistic occupancy heatmaps and a Cox model to better estimate collision risks considering uncertainties and vehicle interactions.

Findings

PORA outperforms existing safety measures in Monte Carlo simulations.

The approach improves real-time collision risk assessment accuracy.

It contributes to safer autonomous vehicle trajectory planning.

Abstract

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 model\edit{,} which considers the relative \edit{motion} 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