Autonomous Vehicle Trajectory Prediction on Multi-Lane Highways Using Attention Based Model

Abstract

The autonomous vehicle anticipates its own behaviour and future trajectory based on the expected trajectories of surrounding vehicles to prevent a potential collision in order to navigate through complex traffic scenarios safely and effectively. The estimated trajectories of surrounding vehicles (target vehicles) are also influenced by past trajectory and positions of its surroundings. In this study, a novel Transformer-based network is used to predict autonomous vehicle trajectory in highway driving. Transformer's multi-head attention method is employed to capture social-temporal interaction between the target vehicle and its surroundings. The performance of the proposed model is compared with Recurrent Neural Network (RNN) based sequential models, using the NGSIM dataset. The results show that the proposed model predicts 5s long trajectory with 10% lower Root-Mean-Square Error (RMSE) than the RNN-based state-of-the-art model.