Optimizing Pretrained Transformers for Autonomous Driving

Engineering notes

Our experimental results in the CARLA simulation environment demonstrate that our approach not only achieves robustness in comparison to previous approaches, but also shows potential for wider application in similar navigation scenarios.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Vision-based end-to-end driving systems have demonstrated impressive capabilities through the utilization of large Transformer architectures. More specifically, researchers have combined Transformers with Imitation Learning, in order to construct agents that learn to map navigation states to actions from large datasets created by experts. Although this approach usually works well, it relies on specific datasets and expert actions, and thus achieving limited generalization capability, which can be quite catastrophic in uncertain navigation environments, such as urban areas. To overcome this limitation, we further expand the training process of the agent by applying the Phasic Policy Gradient algorithm, a Deep Reinforcement Learning (DRL) method that improves its generalization capability by enabling it to explore and interact with the environment. We further enhance our approach by integrating a custom reward function that penalizes the weaknesses of the pretrained agent, alongside with additional DRL techniques to enhance its efficiency and accelerate convergence. Our experimental results in the CARLA simulation environment demonstrate that our approach not only achieves robustness in comparison to previous approaches, but also shows potential for wider application in similar navigation scenarios.

Links and sources

• Official / arXiv page

Comments