SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections
Ethan Zhang Hao Xiao Yiqian Gan Lei Wang
In this work we propose a deep learning model, i.e., SAPI, to predict vehicle
trajectories at intersections. SAPI uses an abstract way to represent and
encode surrounding environment by utilizing information from real-time map,
right-of-way, and surrounding traffic. The proposed model consists of two
convolutional network (CNN) and recurrent neural network (RNN)-based encoders
and one decoder. A refiner is proposed to conduct a look-back operation inside
the model, in order to make full use of raw history trajectory information. We
evaluate SAPI on a proprietary dataset collected in real-world intersections
through autonomous vehicles. It is demonstrated that SAPI shows promising
performance when predicting vehicle trajectories at intersection, and
outperforms benchmark methods. The average displacement error(ADE) and final
displacement error(FDE) for 6-second prediction are 1.84m and 4.32m
respectively. We also show that the proposed model can accurately predict
vehicle trajectories in different scenarios.