TR2024-084
Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data
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- "Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data", European Control Conference (ECC), DOI: 10.23919/ECC64448.2024.10590862, June 2024, pp. 699-704.BibTeX TR2024-084 PDF
- @inproceedings{Wang2024jun4,
- author = {Wang, Yanbing and Berntorp, Karl and Menner, Marcel}},
- title = {Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- pages = {699--704},
- month = jun,
- doi = {10.23919/ECC64448.2024.10590862},
- url = {https://www.merl.com/publications/TR2024-084}
- }
,
- "Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data", European Control Conference (ECC), DOI: 10.23919/ECC64448.2024.10590862, June 2024, pp. 699-704.
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Research Areas:
Abstract:
This paper proposes a technology for road-shape monitoring using crowdsourced vehicle data. The technology uses vehicle measurements and a dynamics model in a statistical estimation framework with a kernel model approximating the road shape. The rationale for considering the vehicle dynamics for road monitoring is that the same road yields different measurements/oscillations for different vehicle types. Next, this paper shows how to use such estimated road shape and vehicle dynamics for semi-active suspension control with the objective to improve passenger comfort. Results using the high-fidelity simulator CarSim show that the proposed technology (i) only needs a few vehicles for estimating the road shape, (ii) can improve passenger comfort by semi-active suspension control, and (iii) is robust to model mismatch indicating the applicability to a real-world scenario.