Multiple Contextual Cues Integrated Trajectory Prediction for Autonomous Driving

摘要

Trajectory prediction is an essential and challenging task for autonomous driving and mobile robots. The main difficulty is to model actor-actor interaction and actor-scene interaction. In addition, the different motion characteristics of each actor also increase the challenge of prediction. Most existing data-driven methods mainly focus on the interaction between actors but ignore the influence of their independent motion characteristics and actor-scene interaction. In this letter, we propose a multiple contextual cues integrated trajectory prediction method. Specifically, an LSTM-based encoder extracts the motion features to express the driving characteristics of each actor. Meanwhile, an attention-based graph module is applied to accurately model interaction behaviors. The scene features are extracted from high-definition vector maps by convolution neural networks. Combining these three types of attribute features, the decoder module then infers the future trajectory. We evaluate the proposed approach on two widely-used datasets, i.e. ApolloScape and Argoverse, and state-of-the-art results demonstrate the effectiveness of our approach.