More research attention has recently been given to end-to-end autonomous
driving technologies where the entire driving pipeline is replaced with a
single neural network because of its simpler structure and faster inference
time. Despite this appealing approach largely reducing the components in
driving pipeline, its simplicity also leads to interpretability problems and
safety issues arXiv:2003.06404. The trained policy is not always compliant with
the traffic rules and it is also hard to discover the reason for the
misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are
also critical to autonomous driving's security and feasibility to perceive the
surrounding environment under complex driving scenarios. In this paper, we
proposed P-CSG, a novel penalty-based imitation learning approach with cross
semantics generation sensor fusion technologies to increase the overall
performance of End-to-End Autonomous Driving. We conducted an assessment of our
model's performance using the Town 05 Long benchmark, achieving an impressive
driving score improvement of over 15%. Furthermore, we conducted robustness
evaluations against adversarial attacks like FGSM and Dot attacks, revealing a
substantial increase in robustness compared to baseline models.More detailed
information, such as code-based resources, ablation studies and videos can be
found at this https URL

提出了一种基于惩罚的模仿学习方法，采用交叉语义生成传感器融合技术来提高端到端自动驾驶的整体性能，并对其在长城 05 城镇基准测试中的性能进行了评估，并显示出相对基准模型的显著增强，同时对抗性攻击也表现出更高的鲁棒性。