TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

TL;DR

TrafficPredict is an LSTM-based real-time trajectory prediction system for heterogeneous traffic agents, improving autonomous vehicle navigation safety by accurately forecasting diverse agent movements in complex urban environments.

Contribution

The paper introduces TrafficPredict, a novel LSTM-based model with instance and category layers for accurate trajectory prediction of various traffic agents in urban settings.

Findings

TrafficPredict achieves higher accuracy than prior methods.

The dataset includes challenging urban traffic scenarios.

The approach effectively models interactions among different agent types.

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.