TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions
This addresses the problem of accurate trajectory prediction in complex urban traffic for applications like autonomous driving, though it is incremental with a novel hybrid method.
The paper tackles trajectory prediction for diverse road agents in dense traffic by modeling heterogeneous interactions, achieving a 30% improvement over state-of-the-art methods on dense datasets.
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%.