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Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Yi, Dewei and Su, Jinya and Liu, Cunjia and Chen, Wen-Hua (2019) 'Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles.' IEEE Transactions on Industrial Informatics, 15 (6). pp. 3693-3702. ISSN 1551-3203

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Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of conventional algorithms using all drivers' data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T intersections by seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike's information criterion are applied to individual drivers trajectories for learning in-depth driving behaviors. Then, various classifiers are evaluated to link low-level vehicle states to high-level driving behaviors. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a real-world driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviors than manually defined maneuver due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.

Item Type: Article
Uncontrolled Keywords: Driver behavior prediction; intelligent vehicle; polynomial regression mixture (PRM); trajectory clustering
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Nov 2019 18:00
Last Modified: 18 Aug 2022 10:28

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