Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

Abstract

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Road Terrain Recognition Based on Tire Noise for Autonomous Vehicle

“…As suggested by Lee et al [23], the testing vehicle was instrumented with a single microphone installed on the front wing of the right-rear wheel proximity, which permits to achieve a better signal to noise ratio (SNR), and prevents noise disturbance from the vehicle's engine, exhaust pipe and other parts. In addition, to avoid the influence of wind, a windscreen was used for the microphone.…”

“…Du et al [22] proposed a method to distinguish the abnormal pavement types using an improved Gaussian background model and KNN algorithm. Lee et al [23] proposed a real-time road surface classification algorithm based on a deep neural work, through a three-axis accelerometer installed inside the tire, and longitudinal and vertical axis acceleration signals can achieve better accuracy. The dynamic response-based method is excellent for road terrain recognition, but its signal acquisition and processing are difficult.…”

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

“…For example, Tumas et al [60] proposed a deep learning model to fix sensor distortion under severe weather conditions. Studies also aimed at classifying the road surface condition in order to improve the performance of Electronic Stability Control (ESC), ACC, and AEB systems [37,52]. Han et al [22] developed a deep learning segmentation algorithm to recognize irregular road areas.…”