论文标题
南加州大学：自动驾驶中面向安全的3D对象探测器的不妥协的空间约束
USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving
论文作者
论文摘要
在这项工作中，我们考虑了3D对象探测器在自动驾驶环境中的安全性能。具体而言，尽管大众文献表现出了令人印象深刻的结果，但开发人员经常发现很难确保安全的基于学习的感知模型的安全部署。将挑战归因于缺乏面向安全指标的挑战，我们特此提出了毫不妥协的空间限制（USC），这表征了一个简单而重要的本地化要求，要求预测从自主工具中看到对象，以完全覆盖对象。当我们使用透视图和鸟类视图制定时，这些约束可以自然地通过定量措施反映出，使得具有更高分数的对象检测器意味着较低的碰撞风险。最后，除了模型评估之外，我们还将定量措施纳入了共同的损失函数，以使现有模型以安全为导向的微调。通过使用Nuscenes数据集和闭环模拟的实验，我们的工作表明了对感知水平上安全概念的考虑，不仅可以将模型性能提高到准确性，而且还允许与实际系统安全性更直接地联系。
In this work, 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.
