Autonomous Vehicles (AVs) have the potential to provide numerous societal
benefits, such as decreased road accidents and increased overall transportation
efficiency. However, quantifying the risk associated with AVs is challenging
due to the lack of historical data and the rapidly evolving technology. This
paper presents a data-driven framework for comparing the risk of different AVs'
behaviors in various operational design domains (ODDs), based on counterfactual
simulations of "misbehaving" road users. We introduce the concept of
counterfactual safety margin, which represents the minimum deviation from
normal behavior that could lead to a collision. This concept helps to find the
most critical scenarios but also to assess the frequency and severity of risk
of AVs. We show that the proposed methodology is applicable even when the AV's
behavioral policy is unknown -- through worst- and best-case analyses -- making
the method useful also to external third-party risk assessors. Our experimental
results demonstrate the correlation between the safety margin, the driving
policy quality, and the ODD shedding light on the relative risk associated with
different AV providers. This work contributes to AV safety assessment and aids
in addressing legislative and insurance concerns surrounding this emerging
technology.

自动驾驶车辆的风险评估是一项具有挑战性的任务，本文提出了一种基于数据驱动的框架，通过对 “不当行为” 道路用户的对照模拟来比较不同自动驾驶车辆在不同操作设计领域的风险。引入了反事实安全余量的概念，用于表示最小偏差与正常行为之间可能导致碰撞的关系，展示了该方法在未知行为策略下仍可适用，从而有助于第三方风险评估，并在自动驾驶车辆供应商之间的相对风险方面提供了洞察。