TR2024-084

Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data


    •  Wang, Y., Berntorp, K., Menner, M., "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}
      • }
  • Research Areas:

    Control, Dynamical Systems, Machine Learning

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.