A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness
This addresses safety assessment needs for regulators and insurers in the autonomous vehicle domain, though it appears to be an incremental methodological contribution.
The paper tackles the problem of evaluating autonomous vehicle risk by proposing a counterfactual safety margin framework that simulates misbehaving road users to quantify collision risks. Experimental results show correlations between safety margins, driving policies, and operational domains, providing insights into relative risks of different AV providers.
Autonomous Vehicles (AVs) promise a range of societal advantages, including broader access to mobility, reduced road accidents, and enhanced transportation efficiency. However, evaluating the risks linked to AVs is complex due to limited historical data and the swift progression of technology. This paper presents a data-driven framework for assessing the risk of different AVs' behaviors in various operational design domains (ODDs), based on counterfactual simulations of "misbehaving" road users. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision. This methodology not only pinpoints the most critical scenarios but also quantifies the (relative) risk's frequency and severity concerning AVs. Importantly, we show that our approach is applicable even when the AV's behavioral policy remains undisclosed, through worst- and best-case analyses, benefiting external entities like regulators and risk evaluators. Our experimental outcomes demonstrate the correlation between the safety margin, the quality of the driving policy, and the ODD, shedding light on the relative risks of different AV providers. Overall, this work contributes to the safety assessment of AVs and addresses legislative and insurance concerns surrounding this burgeoning technology.