TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using
Weighted Interactions
release_dwyjs4tv45aplmshndvscroqfu
by
Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
2019
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,
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%.
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