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:我们开发了一种新颖的框架,以评估在存在外生噪声的情况下的交通标志分类任务的感知风险。我们在自动驾驶环境中考虑了这个问题,其中由于分辨率的提高和到交通标志距离的减少而逐渐改善了视觉输入质量和减少了噪音。使用使用标准分类算法得到的感知统计信息,我们旨在量化感知风险以减轻不完美的视觉观察的影响。通过探索感知输出、它们预期的高层动作和潜在的成本,我们展示了失误的条件风险价值(CVaR)的闭合形式表示。几个案例研究支持了我们提出的方法的有效性。