CNN-LSTMによる周辺車両との相互作用を考慮した軌道予測
Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks
周囲8台の車両との相互作用をCNNで抽出しLSTMで車両ダイナミクスを符号化して、渋滞中の自車の将来軌道を予測する手法を提案した論文。
著者: Xiaoyu Mo, Yang Xing, Chen Lv
分類: cs.RO
原文アブストラクト
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the basic abilities of an autonomous vehicle. In congestion, a vehicle's future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method which predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared weights, and the interaction is extracted with a simple CNN. The proposed model is trained and tested on trajectories extracted from the publicly accessible NGSIM US-101 dataset. Quantitative experimental results show that the proposed model outperforms previous models in terms of root-mean-square error (RMSE). Results visualization shows that the model is able to predict future trajectory induced by lane change before the vehicle operate obvious lateral movement to initiate lane changing.
