A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios

Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.