State-of-the-art object detectors are commonly evaluated based on accuracy
metrics such as mean Average Precision (mAP). In this paper, inspired by the
fact that mAP is not a direct safety indicator, we propose a straightforward
safety metric, especially for 3D object detectors in Autonomous Driving
contexts, by combining the Intersection-over-Ground-Truth (IoGT) measure and a
distance ratio. Subsequently, we formulate a safety-aware loss function by
amending IoGT to commonly used accuracy-oriented loss functions. Our
experiments using models from the MMDetection3D library, the nuScenes dataset,
and an in-house simulation dataset demonstrate that the object detector trained
with our loss function significantly reduces unsafe predictions while staying
performant on accuracy and maintaining good stability in the learning process.

通过结合 IoGT 指标和距离比率，该论文提出了一种直接的安全度量，特别适用于自动驾驶场景下的三维物体检测器，并将 IoGT 改进为常用精度导向的损失函数，实验证明使用该损失函数训练的目标检测器显著降低了不安全预测率，同时保持良好性能和稳定性。

使用 IoGT 和距离度量方法提高自动驾驶中 3D 物体检测器的安全性

Improving the Safety of 3D Object Detectors in Autonomous Driving using IoGT and Distance Measures

Robots need object-level scene understanding to manipulate objects while
reasoning about contact, support, and occlusion among objects. Given a pile of
objects, object recognition and reconstruction can identify the boundary of
object instances, giving important cues as to how the objects form and support
the pile. In this work, we present a system, SafePicking, that integrates
object-level mapping and learning-based motion planning to generate a motion
that safely extracts occluded target objects from a pile. Planning is done by
learning a deep Q-network that receives observations of predicted poses and a
depth-based heightmap to output a motion trajectory, trained to maximize a
safety metric reward. Our results show that the observation fusion of poses and
depth-sensing gives both better performance and robustness to the model. We
evaluate our methods using the YCB objects in both simulation and the real
world, achieving safe object extraction from piles.

本文介绍了一个名为 SafePicking 的系统，它结合了目标级别的建图和学习驱动的运动规划来安全地从堆中提取目标物体，通过使用模拟和真实世界中的 YCB 物体进行评估，结果表明安全度量奖励的深度 Q 网络的学习可以实现目标提取的成功和稳健性提高。