The invention relates to the field of driving lane changing, and discloses a hybrid automatic driving lane changing decision-making method based on deep reinforcement learning, and the method comprises the steps: obtaining observation value information of a target vehicle and an adjacent vehicle from an environment, inputting the observation value information into a Q-network of a DDQN algorithm, and preliminarily selecting an action; the action is verified through a rule-based method, and the stability of the learning model in the initial training stage is further ensured by using a rule-based model; performing instant risk assessment on the verified action execution by using a dynamic learning prediction model; the data experience obtained through final execution is stored in historical driving data for training the LSTM model, the LSTM model is continuously iterated and updated, and the method has the advantages that the safety of lane changing decisions can be comprehensively balanced, and the influence on traffic flow is minimized.
本发明涉及驾驶换道领域,且公开了一种基于深度强化学习的混合自动驾驶换道决策方法,包括从环境中获取目标车辆及其相邻车辆的观测值信息并输入到DDQN算法的Q‑网络中初步选择一个动作;将该动作通过基于规则的方法进行校验,利用基于规则模型进一步确保学习模型的训练初期的稳定性;使用动态学习的预测模型对校验后的动作执行进行即时风险评估;将最终执行得到的数据经验存储在训练LSTM模型的历史驾驶数据中,不断迭代更新LSTM模型,本发明具备能够综合权衡换道决策的安全性以及最小化对交通流的影响的优点。
Hybrid automatic driving lane changing decision-making method based on deep reinforcement learning
一种基于深度强化学习的混合自动驾驶换道决策方法
2024-09-10
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 / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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