A Vehicle Trajectory Prediction Model for Map-Free Scenes Using the Spatiotemporal Attentional Mechanism

The vehicle trajectory prediction is crucial for autonomous driving. The vast majority of existing trajectory prediction schemes depend on high definition (HD) maps. However, the HD knowledge is not invariably valid under many actual traffic scenarios. When map information is unreliable, vehicle trajectory prediction is a fundamental challenge that must be overcome in autonomous driving areas. Over the vehicle–road cooperation, this work has developed a spatiotemporal-attentional-mechanism-based prediction model (STAM-P) for vehicle trajectories under map-free scenarios to precisely forecast future trajectories in the case of unreliable map information. First, a temporal transformer encoder was used to capture and encode the state information of the vehicle at different time steps for extracting the vehicle temporal features of the trajectories. Second, a spatial encoder layer consisting of the static map convolutional layer and the dynamic spatial transformer in the spatial feature extraction layer was designed to capture and encode the interactions between vehicles to obtain the vehicle spatial features of the trajectories. Finally, the obtained temporal–spatial features were inputted into a multimodal decoding layer to decode and complete the trajectory prediction. Experimental results of the Argoverse dataset revealed that the proposed model outperformed other existing map-free prediction schemes and reached the level of other map-based trajectory prediction models in certain performance metrics.