Tire–road interaction addresses safety with respect to braking friction and energy efficiency in the context of rolling resistance. These phenomena are coherent, but their engineering solutions can be contradictory. For example, highly skid-resistant surfaces may not be ideal for fuel economy, but surfaces with low rolling resistance may be prone to skidding. Several experimental and numerical studies have investigated the individual phenomena, but insufficient attention has been paid to studying them coherently. The present study computed braking friction and rolling resistance for various operating parameters and their coherent response for each parameter with the use of a thermomechanical contact algorithm. Micromechanical finite element simulations of a rolling or braking pneumatic tire against selected asphalt concrete surfaces were performed for various operating conditions, such as tire load, inflation pressure, speed, and ambient air and pavement temperatures. The coefficients of braking friction and rolling resistance were found to decrease with the inflation pressure and the temperature and to increase with the wheel load. The braking friction coefficient was found to decrease with the speed, in contrast to the rolling resistance coefficient, which increases with the same parameter. A full-skidding tire registered lower braking friction than a 20% slipping tire. Also, an asphalt surface with higher macrotexture offered higher braking friction and higher rolling resistance, and vice versa.

The purpose of this paper is to provide new experimental evidences about the friction/water depth relationship and to improve the formulation of friction models in terms of consideration of the effect of water depth. Tests are conducted on test tracks. Friction forces are measured by means of a dedicated trailer providing a locked-wheel (full sliding) friction coefficient. The test surfaces are wetted by an on-board wetting system providing water depths varying from 0.1 mm to 1.50 mm. Effect of the road surface texture on the friction/water depth variation is shown and commented. Inputs newly provided by field tests, compared with laboratory tests, are highlighted. The obtained friction/water depths curves are assimilated to Stribeck curves and analyses, assuming conditions of a starved lubricated contact, are conducted to determine the lubrication regimes experienced by the tire/road contact when the road surface changes from dry to wet. A new friction model is formulated using the three-zone description of the tire/road contact area. The formulation is focused on the water drainage term reflected by a so-called hydrodynamic term F_HL. It was assumed that F_HL is a product of elementary functions expressing the respective effects of water depth, speed, tire tread depth, and road surface macrotexture on water drainage. Form of the elementary functions is derived from experimental evidences and consideration of previous friction models. Fitting of the new model to experimental data is shown and comparison with previous models is discussed.

Road skid resistance decreases under traffic and climate actions. For a surface made of bituminous concrete, the skid resistance evolution involves a removal of the bitumen layer and a polishing of the aggregates. Previous researches have been focused on polishing mechanisms and less is known about bitumen removal phase. This paper presents a laboratory study to better understand the behavior of the stone mastic asphalt under simulated actions of traffic. Due to its formulation, this bituminous concrete contains more bitumen than other materials used for road surfacing. Polishing tests are performed on circular cores using tWehner/Schulze machine, which simulates the traffic-induced polishing by means of rolling rubber cones and measures the tire friction by means of sliding rubber pads. Tests are stopped at predefined numbers of passes for friction measurements, 3D cartographies of surface texture and scanning electron microscopic observations of the cores' surface. Statistical tests are performed to identify relevant texture parameters to explain friction evolution. During bitumen removal phase (i.e. before 20,000 passes of polishing), Ssc (average curvature of peaks) and Sdq (average quadratic slope of asperities) are correlated with friction values. After 50,000 passes, volume parameter Vvc is more adapted due to the abrasion of asphalt mix surface. Then, scanning electron microscopic observations show that a layer composed by bitumen and particles (small aggregates and sand) is a third-body surrounding aggregates, which evolves in thickness and size of aggregates during polishing. The system composed of the aggregates, the bitumen–particles layer and the polishing cones can be assimilated to a tribosystem with a tribological circuit at the interface. The bitumen–particles layer acts as an internal flow, before leaving the contact area. The movement of the layer under the shear stress induced by the polishing cones explains the ejection of big aggregates and the heterogeneity of the layer around aggregates.

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.