Bigru Based Online Multi-Modal Driving Maneuvers And Trajectory Prediction

Accurately predicting maneuvers and trajectory of vehicles are essential prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. Motions of each vehicle in a scene is governed by the traffic context, that is, the motion and relative spatial positions of neighboring vehicles, and is also affected by its motion inertia, that is, the trajectory history. In this paper, we propose a novel scheme based on Bidirectional Gated Recurrent Unit (BiGRU) to conduct online multi-modal driving maneuvers and trajectory prediction. The motivation for this BiGRU based method relies on its enhanced prediction accuracy and computational efficiency in outputting the predicted results within the limited prediction horizon. We utilize a BiGRU to extract the complete history and future information of every point in the trajectory history sequence, apply dilated convolutional social (DCS) for learning interdependencies in vehicle motion, and subsequently use a GRU decoder model to make predictions. Additionally, our model simultaneously outputs a multi-modal predictive distribution over future trajectory and vehicle's behavior prediction results. We evaluate our model using the publicly available NGSIM US-101and I-80 datasets. Our results show improvements over the state-of-the-art in terms of Root Mean Square Error (RMSE) values and Negative Log-Likelihoods (NLL). We also present a qualitative analysis of the model's predicted maneuvers and multi-model trajectories for various traffic scenarios.