Motion Planning for Autonomous Driving with Real Traffic Data Validation
Wenbo Chu, Kai Yang, Shen Li, Xiaolin Tang
Abstract
Abstract Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning, which enables safe autonomous driving on public roads. In this paper, a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method. To begin with, a trajectory prediction model is established based on the graph neural network (GNN) that is trained utilizing the INTERACTION dataset. Then, the validated trajectory prediction model is used to predict the future trajectories of surrounding road users, including pedestrians and vehicles. In addition, a GNN prediction model-enabled motion planner is developed based on the model predictive control technique. Furthermore, two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach, i.e . , merging and roundabout scenarios. The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.