This paper deals with the modeling of rolling resistance and the analysis of the effect of pavement texture. The Rolling Resistance Model (RRM) is a simplification of the no-slip rate of the Dynamic Friction Model (DFM) based on modeling tire/road contact and is intended to predict the tire/pavement friction at all slip rates. The experimental validation of this approach was performed using a machine simulating tires rolling on road surfaces. The tested pavement surfaces have a wide range of textures from smooth to macro-micro-rough, thus covering all the surfaces likely to be encountered on the roads. A comparison between the experimental rolling resistances and those predicted by the model shows a good correlation, with an R² exceeding 0.8. A good correlation between the MPD (mean profile depth) of the surfaces and the rolling resistance is also shown. It is also noticed that a random distribution and pointed shape of the summits may also be an inconvenience concerning rolling resistance, thus leading to the conclusion that beyond the macrotexture, the positivity of the texture should also be taken into account. A possible simplification of the model by neglecting the damping part in the constitutive model of the rubber is also noted.

In this work, a new laboratory test method is developed to characterise the relationship between rolling resistance and macrotexture of road surfaces. This test would allow a fast characterisation and comparison of different wearing courses in terms of rolling resistance. Description of the test machine, samples and test protocol is provided. The effect of influential factors such as speed, temperature and surface texture on the test results is evaluated. Particular attention is paid to the relationship between surface macrotexture and rolling resistance. The macrotexture of the samples is characterised using the measured surface profiles. The partial profile envelopment by the rubber and its effect on the results is simulated by applying different filters to the surface profiles. Obtained results show that by considering the envelop profile a good correlation between surface macrotexture and rolling resistance can be observed. The laboratory results are in good agreement with those obtained using the road test.

The paper presents an experimental study conducted to evaluate the feasibility of using accelerometers as an indirect means to estimate water depths on road surfaces. It makes use of the vibration of the vehicle’s wheel arch due to water droplets projected by a tire rolling on a wet road surface. A trailer equipped with a wheel and towed by a van was used. The test setups to spread water on the road surface and before the test wheel, measure the water depth and visualize the water spray are described. The test program, conducted on a test track closed to the traffic, includes three surfaces and two speeds. Visualization of water flows by means of high-speed cameras makes it possible to choose a suitable location for the accelerometers. It turns out that signals provided by the accelerometers are affected by the trailer’s movement; a filtering method has been successfully developed to remove noises. Results show a tight relationship between the mean amplitude of accelerometric signals and actual water depths. Discussions are made in terms of effects of the vehicle speed and the road surface texture. Perspectives for using the developed system to improve passenger safety under autonomous driving conditions are presented.

To investigate the relationship between tire rolling friction and road surface texture, a model based on the energy dissipated by a rubber block when it is deformed cyclically by road surface asperities is proposed. The effects of speed and temperature are considered using time-temperature superposition and the Williams-Landel-Ferry equations. The model is applied to texture profiles, measured using a laser profilometer, and filtered to simulate the envelopment of road surfaces by a tire, to estimate the dissipated energy and deduce the coefficient of rolling friction. The comparison between the model and the experiments, obtained by measurements performed on road samples in the laboratory, shows a fair correlation between the predicted and measured coefficients of rolling friction, this depending on the type of surface studied. Discussions are made to explain the gap between the model and the experiments and identify perspectives for future investigations.

Powered 2-wheeler (PTW) safety remains a critical concern, prompting the investigation into the efficacy of advanced rider assistance systems (ARAS) in reducing crash rates. The objective of this study is to provide a comprehensive quantification of the effects of 2 such systems, Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB), on the avoidance or mitigation of motorcycle crashes. Using real-world motorcycle crash data, this research employed kinematic reconstruction techniques and numerical simulations to evaluate the impact of ACC and AEB on crash reduction. A parametric study was conducted, exploring various parameters including trigger distances, deceleration rates, and field of view, to assess their influence on the effectiveness of this systems in crashes involving PTWs. The analysis revealed significant findings regarding the effectiveness of ACC and AEB in reducing crash rates involving motorcycles. Notably, ACC, especially in dynamic mode with a trigger distance of 40 m, demonstrated the ability to prevent 53% of crashes, resulting in impact speed reductions ranging from 4 to 25 km/h. Similarly, AEB exhibited remarkable effectiveness, reducing impact speeds by 2.5 to 38.9 km/h, with avoidance rates ranging from 7% to 63%. Optimization of key parameters such as triggering strategy, deceleration, sensor range, and field of view is crucial for maximizing the effectiveness of ARAS in motorcycle safety applications. These findings provide valuable insights for manufacturers and policymakers, highlighting the need for tailored approaches to system design and implementation. By refining these systems based on empirical data and analysis, significant improvements can be made in motorcycle safety, ultimately saving lives and reducing the severity of crashes on roadways.

This paper presents an evaluation of a system aiming at estimating water depths on a road surface. Using accelerometers, the system records the vibrations of a wheel arch liner due to impacts of water droplets. The system setup, including the location of the accelerometers on a wheel arch and the data acquisition, is described. Tests were performed with a passenger car on various road surfaces and at different vehicle speeds and water depths. Signals recorded by the accelerometers are filtered and processed. The link between the acceleration amplitude, the water depth, and the vehicle speed is consistent with results from previous studies. The effect of the surface texture is less obvious and needs further investigations. A mathematical model has been developed to relate the acceleration amplitude to the water depth. The potential application of the developed system to on-board evaluation of pavement wetness, and consequently the pavement skid resistance, is discussed. Perspectives for driver assistance, or more generally, for autonomous driving to improve traffic safety, are also highlighted.