TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

TLDR

The authors propose TrafficPredict, an LSTM‑based real‑time traffic‑prediction algorithm, to enable autonomous vehicles to safely and efficiently navigate complex urban traffic by accurately forecasting the trajectories of diverse traffic‑agents. TrafficPredict uses an LSTM architecture with an instance layer that models individual movements and interactions and a category layer that captures similarities among same‑type agents, trained on a large‑city trajectory dataset containing diverse and challenging scenarios. On the new dataset, TrafficPredict achieves higher trajectory‑prediction accuracy than prior methods.

Abstract

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.