AI-TP: Attention-Based Interaction-Aware Trajectory Prediction for Autonomous Driving

Abstract

Despite the advancements in the technologies of autonomous driving, it is still challenging to study the safety of a self-driving vehicle. Trajectory prediction is one core function of an autonomous vehicle. This study proposes an Attention-based Interaction-aware Trajectory Prediction (AI-TP) for traffic agents around the autonomous vehicle. With an encoder-decoder architecture, the AI-TP model uses Graph Attention Networks (GAT) to describe the interactions of traffic agents and Convolutional Gated Recurrent Units (ConvGRU) to carry out predictions. Based on the attention mechanism, the AI-TP model constructs graphs from various traffic scenes to predict trajectories of different types of traffic agents. Traffic data from both the high-way (i.e., NGSIM) and urban road areas (i.e., ApolloScape and Argoverse) are used to evaluate the performance of the AI-TP model. Numerical results demonstrate that the AI-TP model requires less inference time and achieves better prediction accuracy than state-of-the-art methods. Specifically, the AI-TP model improves the performance with much less inference time on the NGSIM dataset, which shows the promise of predicting trajectories under various scenarios. The code of the AI-TP model will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KP-Zhang/AI-TP</uri> .