A Strain-Based Method to Detect Tires’ Loss of Grip and Estimate Lateral Friction Coefficient from Experimental Data by Fuzzy Logic for Intelligent Tire Development

Abstract

Abstract:Tires are a key sub-system of vehicles that have a big responsibility for comfort, fuel consumption and traffic safety. However, current tires are just passive rubber elements which do not contribute actively to improve the driving experience or vehicle safety. The lack of information from the tire during driving gives cause for developing an intelligent tire. Therefore, the aim of the intelligent tire is to monitor tire working conditions in real-time, providing useful information to other systems and becoming an active system. In this paper, tire tread deformation is measured to provide a strong experimental base with different experiments and test results by means of a tire fitted with sensors. Tests under different working conditions such as vertical load or slip angle have been carried out with an indoor tire test rig. The experimental data analysis shows the strong relation that exists between lateral force and the maximum tensile and compressive strain peaks when the tire is not working at the limit of grip. In the last section, an estimation system from experimental data has been developed and implemented in Simulink to show the potential of strain sensors for developing intelligent tire systems, obtaining as major results a signal to detect tire's loss of grip and estimations of the lateral friction coefficient.

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

“…While such a method can be applied to detect friction potential indicators, different levels of pavement roughness still cause undesirable vibration and a negative influence on results. As an alternative to the accelerometer, the strain gauges have been mounted in the inner liner surface of the tyre to characterise the grip [14]. The experimental research showed strong straining parameters relation with tyre lateral force but for limited grip.…”

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

“…The discrete form representations of constraints are straight-forward to define, with the exception of the dynamics constraint (6), which involves first and second order derivatives of r(s) with respect to the arclength s. We use finite differences to approximate r (s),…”

“…Although constantly pursuing high speeds is not the goal of autonomous driving, varying weather conditions may have impact on the road quality in such a way as to dramatically reduce the friction coefficients and in consequence the maximum safe velocity limits for vehicles [4]. Thanks to the rapid development of mature sensing, perception and scene understanding system relying on computer vision and machine learning techniques for autonomous driving, high level information such as weather conditions, road surface categories, together with vehicle state information, can be delivered from on-board perception systems to road friction estimators [5,6]. The environment-dependent, varying friction coefficient becomes available to speed or motion planners online, which can be used to generate safety-guaranteed speed profiles.…”

A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations

“…It was concluded that the results were apparently independent of the position of the strain gauge. Some other researches have successfully developed intelligent tires based on strain gauge measurements, achieving advanced systems to estimate tire characteristics [16,17,18,19,20]. Matsuzaki and Todoroki [1] investigated the relationship between variation in strain on the inner surface of pneumatic tires and tire mechanical parameters by carrying out finite element analysis and simulating the strain sensor signal when a tire rotates.…”