2020/09/25 by Bruno Henrique Groenner Barbosa, Nan Xu, Barbosa, Bruno Henrique Groenner +5
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Vehicle emissions and performance #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.12463
openalex publication_date 2020/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding the dynamic behavior of tires and their interactions with road\nplays an important role in designing integrated vehicle control strategies.\nAccordingly, having access to reliable information about the tire-road\ninteractions through tire embedded sensors is very demanding for developing\nenhanced vehicle control systems. Thus, the main objectives of the present\nresearch work are i. to analyze data from an experimental accelerometer-based\nintelligent tire acquired over a wide range of maneuvers, with different\nvertical loads, velocities, and high slip angles; and ii. to develop a lateral\nforce predictor based on a machine learning tool, more specifically the\nGaussian Process Regression (GPR) technique. It is delineated that the proposed\nintelligent tire system can provide reliable information about the tire-road\ninteractions even in the case of high slip angles. Besides, the lateral forces\nmodel based on GPR can predict forces with acceptable accuracy and provide\nlevel of uncertainties that can be very useful for designing vehicle control\nstrategies.\n