论文标题
Duel Deep Q网络用于自动驾驶汽车的高速公路决策：一个案例研究
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
论文作者
论文摘要
这项工作通过使用深度强化学习（DRL）来优化自动驾驶汽车的公路决策策略。首先，建造了高速公路驾驶环境，其中包括自我车辆，周围的车辆和道路车道。然后，自动化车辆的超车决策问题被制定为最佳控制问题。然后详细阐述了相关的控制措施，状态变量和优化目标。最后，使用深Q网络来得出自我车辆的智能驾驶政策。仿真结果表明，自我车辆可以在学习和培训后安全有效地完成驾驶任务。
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.
