基于贝叶斯网络构建 RoboSim 模型的自动驾驶行为决策

The realization of safe and efficient behavior decision-making has become a challenging issue for autonomous driving. As autonomous driving industries develop vigorously, industrial professionals and academic members have proposed many autonomous driving behavior decision-making approaches. However, due to the influence of environmental uncertainties as well as requirements for effectiveness and high security of the decision, existing approaches fail to take all these factors into account. Therefore, this study proposes an autonomous driving behavior decision-making approach with the RoboSim model based on the Bayesian network. First, based on domain ontology, the study analyzes the semantic relationship between elements in autonomous driving scenarios and predicts the intention of dynamic entities in scenarios by the LSTM model, so as to provide driving scenario information for establishing the Bayesian network. Next, the autonomous driving behavior decision-making in specific scenarios is inferred by the Bayesian network, and the state transition of the RoboSim model is employed to carry the dynamic execution of behavior decision-making and eliminate the redundant operation of the Bayesian network, thus improving the efficiency of decision-making. The RoboSim model is platform-independent. In addition, it can simulate the decision-making cycle and support validation technologies in different forms. To ensure the safety of the behavior decision-making, this study uses a model checking tool UPPAAL to verify and analyze the RoboSim model. Finally, based on lane change and overtaking cases, this study validates the feasibility of the proposed approach and provides a feasible way to achieve safe and efficient autonomous driving behavior decision-making.