Precise Longitudinal Control of Automated Vehicles without Complex Modeling Based on Physical Data

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Authors Abstract
Content
Precise controls of vehicle states are crucial to automated vehicles (AVs). Traditional model-based AV control algorithms require complex modeling and controller design, and their accuracy is still affected a lot by various uncertainties. Latest data-driven controls such as artificial neural network (ANN)-based controls can reduce modeling efforts but are usually subject to robustness issues in unseen scenarios. This article proposes to combine a data-driven control and a typical analytical model-formed control to achieve a better AV longitudinal control performance with fewer modeling efforts. The data-driven control can handle the complex modeling, calibration, and controller design, and the analytical model-formed control can guide the direction of the control with better predictability and robustness in unseen scenarios. The proposed controller is experimentally implemented and validated using a real AV. The performance is compared to the standalone data-driven controller and analytic model-formed controller, and the experimental results demonstrate the effectiveness and advantages of the proposed approach.
Meta TagsDetails
DOI
https://doi.org/10.4271/12-06-03-0020
Pages
12
Citation
Guo, L., and Jia, Y., "Precise Longitudinal Control of Automated Vehicles without Complex Modeling Based on Physical Data," Connected and Automated Vehicles 6(3):309-319, 2023, https://doi.org/10.4271/12-06-03-0020.
Additional Details
Publisher
Published
Feb 17, 2023
Product Code
12-06-03-0020
Content Type
Journal Article
Language
English