The invention discloses a large-scale commercial vehicle safe driving decision-making method in an urban low-speed environment, and the method comprises the steps: firstly collecting the safe driving behaviors of a human driver in an urban traffic environment, and constructing and forming a safe driving behavior data set; secondly, constructing a multi-head attention-based commercial vehicle safe driving decision model; the model comprises a deep double-Q network and a generative adversarial imitation learning sub-network. Wherein the deep double-Q network learns safe driving strategies in edge scenes such as dangerous scenes and conflict scenes through an unsupervised learning mode; and the generative adversarial imitation learning sub-network simulates safe driving behaviors of a human driver under different driving conditions and driving working conditions. And finally, training the safe driving decision model to obtain driving strategies under different driving conditions and driving working conditions. According to the method provided by the invention, the safe driving behavior of a human driver can be simulated, and the influence of factors such as a visual blind area and sudden obstacles on driving safety is considered.
本发明公开了城市低速环境下的大型营运车辆安全驾驶决策方法,首先,采集城市交通环境下人类驾驶员的安全驾驶行为,构建形成安全驾驶行为数据集。其次,构建基于多头注意力的营运车辆安全驾驶决策模型。该模型包含深度双Q网络和生成对抗模仿学习两个子网络。其中,深度双Q网络通过无监督学习的方式,学习危险场景、冲突场景等边缘场景下的安全驾驶策略;生成对抗模仿学习子网络模仿人类驾驶员在不同驾驶条件和行驶工况下的安全驾驶行为。最后,训练安全驾驶决策模型,得到不同驾驶条件和行驶工况下的驾驶策略。本发明提出的方法,能够模拟人类驾驶员的安全驾驶行为,且考虑了视觉盲区、突遇障碍物等因素对行车安全的影响。
Decision-making method for safe driving of large commercial vehicle in urban low-speed environment
城市低速环境下的大型营运车辆安全驾驶决策方法
2022-11-01
Patent
Electronic Resource
Chinese
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