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Heuristic Reinforcement Learning Based Overtaking Decision for an Autonomous Vehicle

机译:基于启发式加强学习自主车辆的超车决策

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This paper proposes an intelligent overtaking decision based on the heuristic reinforcement learning method for an autonomous vehicle. The proposed overtaking control focuses on the safety and efficiency of the autonomous vehicle driving. Firstly, the overtaking problem is modeled and the adaptive safe driving area is constructed. Then, a heuristic reinforcement learning method called Heu-Dyna is developed to derive the optimal overtaking decision, which introduces the heuristic planning function. Besides, the generalized correlation coefficient is designed to evaluate the training perfection of the control strategy. The simulation results show that the performance of the proposed method on the rapidity and optimality is superior to the Q-learning method and the Dyna method. Furthermore, the adaptability of the proposed method is validated by applying different driving conditions.
机译:本文提出了一种基于自主车辆启发式加固学习方法的智能超车决策。 所提出的超车控制侧重于自主车辆驾驶的安全性和效率。 首先,建模超前问题并且构造自适应安全驱动区域。 然后,开发了一种称为Heu-Dyna的启发式增强学习方法,从而推动了最佳的超车决策,这引入了启发式规划功能。 此外,广义相关系数旨在评估控制策略的培训。 仿真结果表明,所提出的方法对快速和最优性的性能优于Q学习方法和DYNA方法。 此外,通过应用不同的驾驶条件来验证所提出的方法的适应性。

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