从安全性和可靠性角度评估物体（误）检测：讨论与度量
Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures
AI总结：
本文提出从安全性和可靠性角度评估物体检测，构建物体关键性模型，基于接近度、方向和相对速度奖励危险物体的检测，并在 nuScenes 数据集上比较九种检测器，发现传统排名最优的检测器在安全视角下未必最佳。
摘要：
我们认为，安全关键领域的物体检测器应优先检测最有可能干扰自动驾驶主体行动的物体。特别是那些可能影响主体安全性和可靠性的物体。为了在自动驾驶背景下量化物体（误）检测对安全性和可靠性的影响，我们提出了新的物体检测度量方法，以奖励对最危险且最可能影响驾驶决策的物体的正确识别。为此，我们构建了一个物体关键性模型，基于与目标车辆的接近程度、方向和相对速度来奖励物体的检测。然后，我们将该模型应用于最近的自动驾驶数据集 nuScenes，并比较了九种物体检测器。结果表明，在多种设置下，根据 nuScenes 排名表现最好的物体检测器，在将焦点转向安全性和可靠性时，并非首选。
英文摘要：
We argue that object detectors in the safety critical domain should prioritize detection of objects that are most likely to interfere with the actions of the autonomous actor. Especially, this applies to objects that can impact the actor's safety and reliability. To quantify the impact of object (mis)detection on safety and reliability in the context of autonomous driving, we propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. To achieve this, we build an object criticality model to reward the detection of the objects based on proximity, orientation, and relative velocity with respect to the subject vehicle. Then, we apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
