融合轨迹预测与多模态数据，提升自动驾驶变道决策安全性
A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
- 用周围车辆轨迹预测补充状态空间，降低变道风险
- 结合图像与传感器数据，构建混合状态空间提升决策合理性
- 端到端控制与强化学习结合，实现完整变道流程
自动驾驶车辆的变道决策是复杂但高回报的行为。本文提出一种基于混合输入的深度强化学习算法，实现车辆在交通流中的抽象变道决策与具体变道动作。首先，提出一种周围车辆轨迹预测方法，降低未来行为对本车的风险，预测结果作为额外信息输入强化学习模型。其次，为全面利用环境信息，模型同时从高维图像和低维传感器数据中提取特征，融合周围车辆轨迹预测与多模态信息作为强化学习的状态空间，提升变道决策的合理性。最后，将强化学习的宏观决策与端到端车辆控制集成，实现完整的变道过程。实验在CARLA模拟器中进行，结果表明，使用混合状态空间显著提升了变道决策的安全性。
原文摘要 · Abstract (English)
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the reinforcement learning model as additional information. Secondly, to comprehensively leverage environmental information, the model extracts feature from high-dimensional images and low-dimensional sensor data simultaneously. The fusion of surrounding vehicle trajectory prediction and multi-modal information are used as state space of reinforcement learning to improve the rationality of lane change decision. Finally, we integrate reinforcement learning macro decisions with end-to-end vehicle control to achieve a holistic lane change process. Experiments were conducted within the CARLA simulator, and the results demonstrated that the utilization of a hybrid state space significantly enhances the safety of vehicle lane change decisions.
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