短時間観測で高速道路の軌道を予測する確率的モデル
On-Demand Trajectory Predictions for Interaction Aware Highway Driving
車間追従モデルを確率化し、わずか400msの観測から他車の軌道を高精度に予測する手法を提案した。
著者: Cyrus Anderson, Ram Vasudevan, Matthew Johnson-Roberson
分類: cs.RO, eess.SP
原文アブストラクト
Highway driving places significant demands on human drivers and autonomous vehicles (AVs) alike due to high speeds and the complex interactions in dense traffic. Merging onto the highway poses additional challenges by limiting the amount of time available for decision-making. Predicting others' trajectories accurately and quickly is crucial to safely executing maneuvers. Many existing prediction methods based on neural networks have focused on modeling interactions to achieve better accuracy while assuming the existence of observation windows over 3s long. This paper proposes a novel probabilistic model for trajectory prediction that performs competitively with as little as 400ms of observations. The proposed model extends a deterministic car-following model to the probabilistic setting by treating model parameters as unknown random variables and introducing regularization terms. A realtime inference procedure is derived to estimate the parameters from observations in this new model. Experiments on dense traffic in the NGSIM dataset demonstrate that the proposed method achieves state-of-the-art performance with both highly constrained and more traditional observation windows.
