Hybrid attention based vehicle trajectory prediction

Accurately predicting trajectory of vehicles is a key capability for autonomous vehicles during real-world driving scenarios, which requires the autonomous vehicles to perceive the surrounding environment and analyze the track histories to judge the driving intentions of the focused surrounding vehicles in order to make decisions for the next trajectory route. In this paper, we propose a hybrid attentional trajectory prediction model incorporating both temporal attentional and spatial attentional mechanisms, which has a temporal attentional model that can be used to capture important temporal information affecting trajectory prediction and a spatial attentional mechanism that can better introduce spatial dependencies and potential driving intention information of surrounding and target vehicles. The models are evaluated for performance on publicly available highD and NGSIM datasets. By comparing the results with other state-of-the-art models for trajectory prediction, it is found that the model achieves an improvement in prediction performance, indicating that this model can more accurately learn driving information from the spatio-temporal perspective of the target driving vehicle’s attention. Further, by qualitatively analyzing the distribution of attention weights from the spatio-temporal perspective, we assessed the interpretability of the model on the maneuverability judgment of the vehicle.