Bai, W., Yu, L., Weightman, A. et al. (4 more authors) (2024) IMTP: Intention-Matching Trajectory Prediction for Autonomous Vehicles. In: Proceedings of 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 21-24 Nov 2023, Queenstown, New Zealand. Institute of Electrical and Electronics Engineers (IEEE) ISBN 979-8-3503-2562-1
Abstract
Trajectory prediction for surrounding vehicles is critical for ensuring the safety of autonomous driving. In this paper, we introduce a novel prediction framework named Intention-Matching Trajectory Prediction (IMTP). Different from existing results that predict trajectories based on only environmental information and historical trajectories, the proposed method initially identifies the possible intentions of surrounding vehicles based on the environment and generates intention-informed trajectories based on the physical vehicle model. Historical trajectories are then used to identify the intention and trajectory with the highest probability. The proposed framework effectively integrates the physical vehicle model, road-related environmental factors, and interactions among surrounding vehicles. A comparative study conducted on a public dataset demonstrates that our framework enhances both prediction accuracy and robustness.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | trajectory prediction, autonomous vehicles |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number UKRI (UK Research and Innovation) Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Nov 2024 11:52 |
Last Modified: | 18 Nov 2024 11:52 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/m2vip58386.2023.10413410 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219746 |