  Safety and performance are key enablers for autonomous driving: on the one
hand we want our autonomous vehicles (AVs) to be safe, while at the same time
their performance (e.g., comfort or progression) is key to adoption. To
effectively walk the tight-rope between safety and performance, AVs need to be
risk-averse, but not entirely risk-avoidant. To facilitate safe-yet-performant
driving, in this paper, we develop a task-aware risk estimator that assesses
the risk a perception failure poses to the AV's motion plan. If the failure has
no bearing on the safety of the AV's motion plan, then regardless of how
egregious the perception failure is, our task-aware risk estimator considers
the failure to have a low risk; on the other hand, if a seemingly benign
perception failure severely impacts the motion plan, then our estimator
considers it to have a high risk. In this paper, we propose a task-aware risk
estimator to decide whether a safety maneuver needs to be triggered. To
estimate the task-aware risk, first, we leverage the perception failure -
detected by a perception monitor - to synthesize an alternative plausible model
for the vehicle's surroundings. The risk due to the perception failure is then
formalized as the "relative" risk to the AV's motion plan between the perceived
and the alternative plausible scenario. We employ a statistical tool called
copula, which models tail dependencies between distributions, to estimate this
risk. The theoretical properties of the copula allow us to compute probably
approximately correct (PAC) estimates of the risk. We evaluate our task-aware
risk estimator using NuPlan and compare it with established baselines, showing
that the proposed risk estimator achieves the best F1-score (doubling the score
of the best baseline) and exhibits a good balance between recall and precision,
i.e., a good balance of safety and performance.
