The invention relates to a reinforcement learning multi-lane driving decision-making method for a dynamic traffic environment. The method comprises the following steps: establishing a decision-making neural network model by using a state space, an action space and track sampling information in a multi-lane driving decision-making scene; performing reinforcement learning training on the decision neural network model by using a comprehensive reward function; the method comprises the following steps: continuously and deeply sensing the surrounding environment through a sensor array arranged on a vehicle, and capturing sensed environment information; the perceived environment information is input into the decision neural network model, and the decision neural network model predicts a vehicle track and recommended driving operation within a period of time according to the current environment information; and the recommended driving operation output by the decision neural network model is converted into a specific control instruction, and the instruction is sent to a drive-by-wire chassis and an actuator of the vehicle. By means of the method, multi-lane automatic driving of the vehicle can be achieved, dependence on external data and a high-precision map is not needed, and independence and stability of the system are improved.
本发明涉及一种面向动态交通环境的强化学习多车道驾驶决策方法,包括:利用多车道驾驶决策的场景中状态空间、动作空间和轨迹采样信息建立决策神经网络模型;利用综合奖励函数对所述决策神经网络模型进行强化学习训练;通过车辆配备的传感器数组对周围环境进行连续而深入的感知,捕获感知的环境信息;将感知的环境信息输入到所述决策神经网络模型中,决策神经网络模型根据当前的环境信息预测一段时间内的车辆轨迹和推荐的驾驶操作;将决策神经网络模型输出的推荐驾驶操作转化为具体的控制指令,并将指令发送至车辆的线控底盘和执行器。通过本发明方法使车辆可以实现多车道自动驾驶,而无需依赖外部数据和高精度地图,提高了系统的独立性和稳定性。
Reinforcement learning multi-lane driving decision-making method for dynamic traffic environment
面向动态交通环境的强化学习多车道驾驶决策方法
2024-12-13
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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