This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance.

This paper describes the conception of a high level, compact, scalable, and long autonomy perception and localization system for autonomous driving applications. Our benchmark is composed of a high resolution lidar (128 channels), a stereo global shutter camera, an inertial navigation system, a time server, and an embedded computer. In addition, in order to acquire data and build multi-modal datasets, this system embeds two perception algorithms (RBNN detection, DCNN detection) and one localization algorithm (lidar-based localization) to provide real-time advanced information such as object detection and localization in challenging environments (lack of GPS). In order to train and evaluate the perception algorithms, a dataset is built from 10,000 annotated lidar frames from various drives carried out under different weather conditions and different traffic and population densities. The performances of the three algorithms are competitive with the state-of-the-art. Moreover, the processing time of these algorithms are compatible with real-time autonomous driving applications. By providing directly accurate advanced outputs, this system might significantly facilitate the work of researchers and engineers with respect to planning and control modules. Thus, this study intends to contribute to democratizing access to autonomous vehicle research platforms.