  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.
