Lane Change Strategies for Autonomous Vehicles: A Deep Reinforcement Learning Approach Based on Transformer

Engineering notes

Finally, the proposed method was evaluated in three lane change scenarios to validate its superiority.

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

End-to-end approaches are one of the most promising solutions for autonomous vehicles (AVs) decision-making. However, the deployment of these technologies is usually constrained by the high computational burden. To alleviate this problem, we proposed a lightweight transformer-based end-to-end model with risk awareness ability for AV decision-making. Specifically, a lightweight network with depth-wise separable convolution and transformer modules was firstly proposed for image semantic extraction from time sequences of trajectory data. Then, we assessed driving risk by a probabilistic model with position uncertainty. This model was integrated into deep reinforcement learning (DRL) to find strategies with minimum expected risk. Finally, the proposed method was evaluated in three lane change scenarios to validate its superiority.

Links and sources

• Official / arXiv page

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