The automotive industry’s journey towards fully autonomous vehicles brings more and more vehicle control systems. Additionally, the reliability and robustness of all these systems must be guaranteed for all road and weather conditions before release into the market. However, the ever-increasing number of such control systems, in combination with the number of road and weather conditions, makes it unfeasible to test all scenarios in real life. Thus, the performance and robustness of these systems needs to be proven virtually, via vehicle simulations.
The key challenge for performing such a range of simulations is that the tire performance is significantly affected by the road/weather conditions. An end user must therefore have access to the corresponding tire models. The current solution is to test tires under all road surfaces and operating conditions and then derive a set of model parameters from measurements. The key disadvantages of this approach are high costs and turnaround times. Furthermore, the validity of the model is limited to the tested operating conditions.
This paper describes an alternative approach where a physics-based adjustment tool – developed in co-operation with Hyundai Motor Company (HMC) – allows for tire models parameterized on a high-friction surface (e.g., asphalt) to be adjusted to snow, scraped ice, and polished ice conditions, without additional measurements. The tool delivers qualitatively correct tire friction characteristics, thus allowing for the digital robustness and performance evaluations of control systems. The potential of this methodology is demonstrated through both single tire measurement and full vehicle testing validation activities.
