Vehicle Trajectory Prediction Based on Multivariate Interaction Modeling
Dongxian Sun, Hongwei Guo, Wuhong Wang
Abstract
Vehicle trajectory prediction is crucial for the safe driving of the intelligent and connected vehicle, but the existing researches suffer from inadequate interaction modeling, especially lacking modeling for dynamic interaction. To solve this problem, we propose a vehicle trajectory prediction model SDT-ATT based on multivariate interaction modeling. Firstly, a hierarchical extracting module is constructed based on the bi-directional long short-term memory network (Bi-LSTM) to extract the contexts of the target vehicle parameters, neighbor vehicle spatial parameters and neighbor vehicle dynamic parameters, respectively. Secondly, the temporal, spatial and dynamic interactions of vehicles are modeled and learned based on the multi-head attention (MHA) mechanism, then fused to obtain the multivariate interaction features. Next, a direct multi-step prediction module is constructed based on long short-term memory network (LSTM), which directly outputs the future coordinates of the vehicle in the target prediction horizons. Finally, the vehicle trajectory prediction model SDT-ATT proposed in this paper is validated based on the public naturalistic driving datasets I-80 and US-101. The data results show that compared with the CS-LSTM model, the SDT-ATT has 9.3% lower ADE average value, 8.5% lower FDE average value and 8.7% lower RMSE average value, and the inference speed of the SDT-ATT model meets the real-time requirement.