IPrism: Characterize and Mitigate Risk by Quantifying Change in Escape Routes

Abstract

This paper addresses the challenge of ensuring the safety of autonomous vehicles (AVs, also called ego actors) in real-world scenarios where AVs are constantly interacting with other actors. To address this challenge, we introduce iPrism which incorporates a new risk metric - the Safety-Threat Indicator (STI). Inspired by how experienced human drivers proactively mitigate hazardous situations, STI quantifies actor-related risks by measuring the changes in escape routes available to the ego actor. To actively mitigate the risk quantified by STI and avert accidents, iPrism also incorporates a reinforcement learning (RL) algorithm (referred to as the Safety-hazard Mitigation Controller (SMC)) that learns and implements optimal risk mitigation policies. Our evaluation of the success of the SMC is based on over 4800 NHTSA-based safety-critical scenarios. The results show that (i) STI provides up to 4.9 x longer lead-time-for-mitigating-accidents compared to widely-used safety and planner-centric metrics, (ii) SMC significantly reduces accidents by 37% to 98 % compared to a baseline Learning-by-Cheating (LBC) agent, and (iii) in comparison with available state-of-the-art safety hazard mitigation agents, SMC prevents up to 72.7% of accidents that the selected agents are unable to avoid. All code, model weights, and evaluation scenarios and pipelines used in this paper are available at: https://zenodo.orgldoi/10.5281/zenodo.10279653.