TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

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.

arXiv commentAccepted by AAAI(Oral) 2019

References in corpus (1)

• AutoRVO: Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic

Cited by in corpus (11)

• Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
• Database Meets Deep Learning: Challenges and Opportunities
• Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video
• JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
• Generative Adversarial Networks for Spatio-temporal Data: A Survey
• A Novel Graph based Trajectory Predictor with Pseudo Oracle
• CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces
• SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
• A Unified Longitudinal Trajectory Dataset for Automated Vehicle
• ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer
• OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots