TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8483-8492

Abstract


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, bi-cycles, 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 account 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%.

Related Material


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[bibtex]
@InProceedings{Chandra_2019_CVPR,
author = {Chandra, Rohan and Bhattacharya, Uttaran and Bera, Aniket and Manocha, Dinesh},
title = {TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}