TR2021-058
Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter
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- "Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter", American Control Conference (ACC), DOI: 10.23919/ACC50511.2021.9483251, May 2021, pp. 160-165.BibTeX TR2021-058 PDF
- @inproceedings{Berntorp2021may,
- author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
- title = {Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter},
- booktitle = {American Control Conference (ACC)},
- year = 2021,
- pages = {160--165},
- month = may,
- doi = {10.23919/ACC50511.2021.9483251},
- url = {https://www.merl.com/publications/TR2021-058}
- }
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- "Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter", American Control Conference (ACC), DOI: 10.23919/ACC50511.2021.9483251, May 2021, pp. 160-165.
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Abstract:
This paper addresses the center-of-gravity height and dynamics estimation problem, which is key for rollover prevention systems in automotive. We model the vehicle as a spring-damper system and develop a Bayesian method that outputs estimates of the center-of-gravity height, suspension stiffness and damping coefficient. We leverage the model structure to design a computationally efficient particle filter, which, combined with Bayesian optimization for particle initialization and a particle-size adaptation scheme, leads to an implementation that provides accurate, smooth estimates of CoG height, stiffness, and damping. A Monte-Carlo simulation study on a standardized maneuver shows that the method almost instantaneously provides reliable estimates that represent well the true parameter values.