グラフと再帰型ニューラルネットワークによる高速道路走行の車両軌道予測
Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving
周囲車両との相互作用を有向グラフで表し、RNNで車両の動態特徴を抽出するGNN-RNNエンコーダ・デコーダで、台数が変動する状況でも対象車両の将来軌道を予測する手法を提案した。
著者: Xiaoyu Mo, Yang Xing, Chen Lv
分類: cs.RO
原文アブストラクト
Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving vehicles. However, a vehicle's future trajectory prediction is a challenging task since it is affected by the social interactive behaviors of neighboring vehicles, and the number of neighboring vehicles can vary in different situations. This work proposes a GNN-RNN based Encoder-Decoder network for interaction-aware trajectory prediction, where vehicles' dynamics features are extracted from their historical tracks using RNN, and the inter-vehicular interaction is represented by a directed graph and encoded using a GNN. The parallelism of GNN implies the proposed method's potential to predict multi-vehicular trajectories simultaneously. Evaluation on the dataset extracted from the NGSIM US-101 dataset shows that the proposed model is able to predict a target vehicle's trajectory in situations with a variable number of surrounding vehicles.
