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Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.

This paper is part of the European OWHEEL project. It proposes a method to improve the comfort of a vehicle by adaptively controlling the Camber and Toe angles of a rear suspension. The purpose is achieved through two actuators for each wheel, one that allows to change the Camber angle and the other the Toe angle. The control action is dynamically determined based on the error between the reference angle and the actual angles. The reference angles are not fixed over time but dynamically vary during the maneuver. The references vary with the aim of maintaining a Camber angle close to zero and a Toe angle that follows the trajectory of the vehicle during the curve. This improves the contact of the tire with the road. This solution allows the control system to be used flexibly for the different types of maneuvers that the vehicle could perform. An experimentally validated sports vehicle has been used to carry out the simulations. The original rear suspension is a Trailing-arm suspension.

An important problem of recent active safety applications is data acquisition for parameters of tire-road interaction, especially coefficient of friction or specific forces in contact patch. Analysis of present solutions in this field allows setting off the virtual and hardware-based determination of tire grip properties. The virtual procedures can be subclassified into Dynamics simulation method Statistical method Fuzzy logic method. The hardware-based procedures are connected with On-board sensors of direct tire grip measurement On-board sensors of indirect tire grip measurement Off-board (on-road) sensors. For above mentioned variants the appropriate engineering solutions are considered in the paper. The long-term approach in road identification centers on active safety applications with on-road sensors, which are integrated in intelligent transportation systems (ITS). The paper proposes conceptual structure for this system.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.