The invention discloses an automatic driving reinforcement learning decision-making and planning method and system based on human-like characteristics. The decision-making and planning method comprises the following steps: determining and quantifying a driving style suitable for an autonomous vehicle under a current condition by analyzing a driving style of a traffic participant; behavior strategies of the autonomous vehicle are trained through an A2C algorithm, namely straight lane keeping/following, left lane changing and right lane changing; generating a human-like safety track in a path speed decoupling mode according to the driving style and the behavior decision; according to the method, attributes of other traffic participants are captured so as to guide an automatic driving automobile to design a safe and efficient track similar to human beings in a social compatible mode, and balance between safety and efficiency is achieved in multiple complex scenes. Hierarchical behavior and motion planning establishes a driving task as an advanced behavior decision process that emphasizes efficiency and a low-level motion planning method that preferentially considers safety.
本发明中公开一种基于类人特征的自动驾驶强化学习决策与规划方法及系统,所述决策与规划方法通过分析交通参与者的驾驶风格来确定和量化当前情况下适合自主车辆的驾驶风格;通过A2C算法训练自主车辆的行为策略,即直行车道保持/跟随、左换道和右换道;遵从所述驾驶风格和行为决策,以路径速度解耦的方式生成类人安全的轨迹;本发明所述方法捕捉了其他交通参与者的属性,以指导自动驾驶汽车以社会兼容的方式设计类似人类的、安全高效的轨迹,在复杂的多场景中实现安全与效率之间的平衡。分层行为和运动规划将驾驶任务确立为强调效率的高级行为决策过程,以及优先考虑安全的低级运动规划方法。
Automatic driving reinforcement learning decision-making and planning method and system based on human-like features
基于类人特征的自动驾驶强化学习决策与规划方法及系统
2023-11-24
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
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