We consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literature, developers often find it hard to ensure the safe deployment of these learning-based perception models. Attributing the challenge to the lack of safety-oriented metrics, we hereby present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement demanding the predictions to fully cover the objects when seen from the autonomous vehicle. The constraints, as we formulate using the perspective and bird's-eye views, can be naturally reflected by quantitative measures, such that having an object detector with a higher score implies a lower risk of collision. Finally, beyond model evaluation, we incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models. With experiments using the nuScenes dataset and a closed-loop simulation, our work demonstrates such considerations of safety notions at the perception level not only improve model performances beyond accuracy but also allow for a more direct linkage to actual system safety.

翻译：我们关注自动驾驶背景下三维目标检测器的安全导向性能。尽管大量文献展示了显著成果，开发人员通常难以确保这些基于学习的感知模型的安全部署。将这一挑战归因于缺乏安全导向指标，我们提出不妥协空间约束（USC），它刻画了一个简单却至关重要的定位要求：从自动驾驶车辆视角观察时，预测结果必须完全覆盖目标物体。通过透视图和鸟瞰图构建的约束可自然由定量指标反映，使得检测器得分越高意味着碰撞风险越低。最终，除模型评估外，我们将定量指标融入常见损失函数，实现对现有模型的安全导向微调。基于nuScenes数据集和闭环仿真的实验证明，在感知层面纳入安全概念不仅能提升模型超越精度的性能，还能更直接地关联实际系统安全性。