政策予測と最適化推論を組み合わせたオンライン車両軌道予測
Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning
LSTMで車両の運転方針を予測し、その方針をガイドに最適化ベースで複雑な交通状況を考慮した連続軌道を生成する二段階のオンライン軌道予測手法を提案した。
著者: Wenchao Ding, Shaojie Shen
分類: cs.RO, cs.LG
原文アブストラクト
In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents. Our method combines high-level policy anticipation with low-level context reasoning. We leverage a long short-term memory (LSTM) network to anticipate the vehicle's driving policy (e.g., forward, yield, turn left, turn right, etc.) using its sequential history observations. The policy is then used to guide a low-level optimization-based context reasoning process. We show that it is essential to incorporate the prior policy anticipation due to the multimodal nature of the future trajectory. Moreover, contrary to existing regression-based trajectory prediction methods, our optimization-based reasoning process can cope with complex contextual factors. The final output of the two-level reasoning process is a continuous trajectory that automatically adapts to different traffic configurations and accurately predicts future vehicle motions. The performance of the proposed framework is analyzed and validated in an emerging autonomous driving simulation platform (CARLA).
