確率的交通シーン予測のための統合フレームワーク
A Framework for Probabilistic Generic Traffic Scene Prediction
複数の交通参加者の連続的な相互作用を運転シーン全体で同時に予測し、各相互作用の継続時間も推定する階層型確率フレームワークを提案した論文。
著者: Yeping Hu, Wei Zhan, Masayoshi Tomizuka
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.
