What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving
This addresses safety issues in autonomous driving for real-world applications, but it is incremental as it builds on existing imitation learning approaches.
The paper tackled the problem of traffic rule non-compliance in end-to-end autonomous driving by proposing P-CSG, a penalty-based imitation learning method with cross semantics generation, resulting in driving score improvements of 8.5% and 2.0% on CARLA benchmarks.
End-to-end autonomous driving, where the entire driving pipeline is replaced with a single neural network, has recently gained research attention because of its simpler structure and faster inference time. Despite this appealing approach largely reducing the complexity in the driving pipeline, it also leads to safety issues because the trained policy is not always compliant with the traffic rules. In this paper, we proposed P-CSG, a penalty-based imitation learning approach with contrastive-based cross semantics generation sensor fusion technologies to increase the overall performance of end-to-end autonomous driving. In this method, we introduce three penalties - red light, stop sign, and curvature speed penalty to make the agent more sensitive to traffic rules. The proposed cross semantics generation helps to align the shared information of different input modalities. We assessed our model's performance using the CARLA Leaderboard - Town 05 Long Benchmark and Longest6 Benchmark, achieving 8.5% and 2.0% driving score improvement compared to the baselines. Furthermore, we conducted robustness evaluations against adversarial attacks like FGSM and Dot attacks, revealing a substantial increase in robustness compared to other baseline models. More detailed information can be found at https://hk-zh.github.io/p-csg-plus.