TraPHic: 重み付き相互作用を用いた密集・異種交通における軌道予測
TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions
バス・車・スクーター・自転車・歩行者が混在する密集交通の動画から、LSTM-CNNハイブリッドネットワークで近未来の軌道を予測する手法を提案し、新たなアジア都市交通データセットも公開した。
著者: Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a new algorithm for predicting the near-term trajectories of road-agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road-agents may correspond to buses, cars, scooters, bicycles, or pedestrians. We model the interactions between different road-agents using a novel LSTM-CNN hybrid network for trajectory prediction. In particular, we take into account heterogeneous interactions that implicitly accounts for the varying shapes, dynamics, and behaviors of different road agents. In addition, we model horizon-based interactions which are used to implicitly model the driving behavior of each road-agent. We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories. We outperform state-of-the-art methods on dense traffic datasets by 30%.
