提出综合多因素的感知安全评分，评估自动驾驶物体检测可靠性。
A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems
- 融合速度、距离、尺寸等参数构建统一安全评分
- 在真实与虚拟数据集上验证，优于现有主流指标
- 适合自动驾驶系统安全评估与算法对比
完全感知环境及其正确解读对自动驾驶车辆至关重要。物体感知是汽车周围感知的主要组成部分。尽管已有多种对象感知评估指标，但对象的重要性因速度、朝向、距离、大小或误检可能导致的碰撞损害而异。因此，安全评估需考虑这些附加参数。本文提出一种新安全度量，整合所有相关参数，为对象感知返回单一且易于理解的安全评估分数。该度量在真实世界和虚拟数据集上进行了评估，并与最先进指标进行了比较。
原文摘要 · Abstract (English)
Complete perception of the environment and its correct interpretation is crucial for autonomous vehicles. Object perception is the main component of automotive surround sensing. Various metrics already exist for the evaluation of object perception. However, objects can be of different importance depending on their velocity, orientation, distance, size, or the potential damage that could be caused by a collision due to a missed detection. Thus, these additional parameters have to be considered for safety evaluation. We propose a new safety metric that incorporates all these parameters and returns a single easily interpretable safety assessment score for object perception. This new metric is evaluated with both real world and virtual data sets and compared to state of the art metrics.
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