Application of a Machine Learning Method to Evaluate Road Roughness from Connected Vehicles

Abstract

Response-type methods have been widely used for road roughness evaluation. However, an important limitation is that they require calibration to account for response and speed variations among instrumented vehicles. The findings of this research obviate the need for calibration by applying a machine learning technique to estimate a roughness category and a roughness index from inertial sensors aboard at least two connected vehicles. The method leverages the future availability of inertial sensor data feeds from connected vehicles. The approach offers an alternative to specifically instrumented vehicles and specially trained technicians. In lieu of data from actual connected vehicles, the authors validated the method by numerical simulations using a model of the vehicle suspension system and a mathematical representation of the road roughness profile. Solving the dynamic response model as a function of various levels of roughness excitation and suspension parameters produced vertical acceleration signals. Subsequently, speed normalization and a convolution of the vertical acceleration responses from at least two simulated vehicles produced a common signal for feature extraction. Finally, a feature selection algorithm provided the most impactful features for a machine learning algorithm to train, test, and classify into a roughness category, and to estimate the international roughness index. Results show that the classification and estimation accuracy exceed 9%.