时空轨迹数据驱动的自动驾驶场景元建模方法

In the current autonomous driving scenario modeling and simulation field, spatio-temporal trajectory data-driven modeling and application of autonomous driving safety-critical scenario are key problems, which is significant to improve the security of the system. In recent years, great progress has been achieved in the modeling and application of spatio-temporal trajectory data, and the application of spatio-temporal trajectory data in specific fields has attracted wide attention. However, due to spatio-temporal trajectory data has diversity and complexity as well as massive, heterogeneous, dynamic characteristics, researches in the safety-critical field modeling still face challenges, including unified meta-data of spatio-temporal trajectory, meta-modeling method based on spatio-temporal trajectory data, data processing based on the data analysis of spatio-temporal trajectory, and data quality evaluation. In view of the scenario modeling requirements in the field of autonomous driving, a meta-modeling approach is proposed to construct spatio-temporal trajectory meta-data based on MOF meta-modeling system. According to the characteristics of spatio-temporal trajectory data and autonomous driving domain knowledge, a meta-model of spatio-temporal trajectory data is constructed. Then, the modeling approach of autonomous driving safety-critical scenarios is studied based on spatio-temporal trajectory data element modeling technology system, a scenario modeling language ADSML is used to automatic instantiation safety-critical scenarios, and a library of safety-critical scenarios is constructed, aiming to provide a feasible approach for the modeling of such safety-critical scenarios. Combined with the scenario of lane change and overtaking, the effectiveness of spatio-temporal trajectory data-driven autonomous driving safety-critical scenario meta-modeling approach is demonstrated, which lays a solid foundation for the construction, simulation, and analysis of the scene model.