Implicit scene context-aware interactive trajectory prediction for autonomous driving

Lan, W., Li, D., Hao, Q., Zhao, D. and Tian, B. (2023) Implicit scene context-aware interactive trajectory prediction for autonomous driving. IEEE Transactions on Intelligent Vehicles, (doi: 10.1109/TIV.2023.3342202) (Early Online Publication)

Abstract

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions; 2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories; 3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grants 52272419 and 62261160654, and in part by the National Key Research and Development Program of China under Grant 2022YFB4703700.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Lan, W., Li, D., Hao, Q., Zhao, D., and Tian, B.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Intelligent Vehicles
• Publisher: IEEE
• ISSN: 2379-8858
• ISSN (Online): 2379-8904
• Published Online: 13 December 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Intelligent Vehicles 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy