Tire-Road Friction Estimation and Classification Based on a CNN using Tire Acoustical Signals for Autonomous Driving Vehicles

With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on Estimation and Prediction of the Tire-Road Friction and Classification, which is one of the most important factors for vehicle state recognition, has not yet been conducted. If Estimation and Prediction of the Tire-Road Friction and Classification are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, estimation and Prediction of the Tire-Road Friction and Classification is essential. To achieve this, tire–pavement interaction noise (TPIN) is adopted as a data source for Estimation and Prediction of the Tire-Road Friction and Classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimension image through time–frequency analysis. Converted images were used together with a convolutional neural network (CNN) architecture to examine the feasibility of theEstimation and Prediction of the Tire-Road Friction and Classification system.