Probabilistic Long-term Vehicle Trajectory Prediction via Driver Awareness Model

Making long-term trajectory prediction accurately for surrounding vehicles is the crucial prerequisite for intelligent vehicles to accomplish superb decision making and motion planning. In this paper, to achieve high-quality prediction accuracy both in the short and long term, we propose an integrated probabilistic framework with the combination of driver awareness model and Gaussian process model. The former model can obtain high-level semantic information using low-level two-dimensional motion elements. And the latter incorporates the vehicle physical model to reach good prediction performance with strengthened historical input sequence. Furthermore, experiments on the public naturalistic driving dataset in lane-changing scenarios are conducted to verify our novel approach. Compared with another advanced method, the superiorities of our proposed approach are demonstrated with higher estimation and prediction accuracy, as well as more reasonable uncertainty description in terms of the whole prediction process.