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.

翻译：我们提出了一种新颖的框架，用于评估在外生噪声存在下交通标志分类任务中感知错误的风险。我们在自动驾驶场景中考虑该问题，其中随着分辨率提高以及距离交通标志的缩短，视觉输入质量逐渐改善。利用标准分类算法获得的估计感知统计量，我们旨在量化感知错误的风险，以减轻不完美视觉观测的影响。通过探索感知输出、预期的高层动作及潜在成本，我们推导出感知错误的条件风险价值（CVaR）的闭式表示。多个案例研究验证了我们所提方法的有效性。