Symbolic Perception Risk in Autonomous Driving
This addresses safety concerns in autonomous driving by providing a method to assess and mitigate misperception risks, though it is incremental as it builds on existing classification algorithms and risk metrics.
The paper tackles the problem of misperception risk in traffic sign classification for autonomous driving by developing a framework to quantify this risk using perception statistics and conditional value-at-risk (CVaR), with case studies demonstrating its effectiveness.
We develop a novel framework to assess the risk of misperception in a traffic sign classification task in the presence of exogenous noise. We consider the problem in an autonomous driving setting, where visual input quality gradually improves due to improved resolution, and less noise since the distance to traffic signs decreases. Using the estimated perception statistics obtained using the standard classification algorithms, we aim to quantify the risk of misperception to mitigate the effects of imperfect visual observation. By exploring perception outputs, their expected high-level actions, and potential costs, we show the closed-form representation of the conditional value-at-risk (CVaR) of misperception. Several case studies support the effectiveness of our proposed methodology.