Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

翻译：近年来，深度神经网络在自动驾驶技术中的应用引起了广泛研究兴趣。这一趋势始于数年前的感知与预测任务，并逐步延伸至运动规划领域。尽管网络性能随时间不断提升，但深度神经网络规划器仍继承深度学习的固有缺陷。基于学习的规划器在训练数据集上难以达到完美精度，且网络性能易受分布外问题影响。本文提出FusionAssurance——一种新颖的基于轨迹的端到端驾驶融合框架，该框架结合物理信息控制实现安全保障。通过将势场引入模型预测控制，FusionAssurance能够应对训练数据集中未包含的场景以及神经网络泛化失效的工况。在CARLA基准测试的多种场景下开展的大量实验验证了该方法有效性。