Tread Depth Monitoring for Automotive Tires

Matthias Kreschmann (Continental Automotive Technologies GmbH)･Nicolas Guinart･Remi Gobin (Continental SCR)

The Tread Depth Monitoring system determines tire tread depth during vehicle operation by measuring wheel rotational speed, vehicle speed, and dynamic radius, considering tire temperature, pressure, and load. It uses sensors for rotational speed and vehicle speed, with dynamic radius calculations based on tire parameters. The system issues warnings for low tread depth, potentially triggering automatic service alerts and communicating with driver assistance systems. It uses traditional tires sensors for temperature and pressure, ensuring accurate tread depth determination and optimized vehicle operation.

Towards a multi-performance real time capable tire model

Christian Ludwig･Benjamin Rieff (Cosin scientific software AG)

FTire was first used in durability simulation, but was soon used to simulate other performances "Ride, NVH, Wear, Handling including thermal dependent friction and structural stiffness". The evaluation of tire performance on a driving simulator requires a highly accurate real time capable multi-performance tire model to give test drivers the same possibilities as on track. This paper introduces a new methodology to generate the complex steering wheel undulation obtained when driving across a real road surface. We will present further first results of the next step where tire-suspension interaction occurring at limit handling will be addressed.

A Tire Model Extension To Express Water Depth and Velocity Influences on Tire Performance

Toshiyuki Hyuga (Siemens)･Carlo Lugaro (Siemens Industry Software and Servises B.V.)

The need for virtualization is increasing in tire and vehicle development. The performance on wet road such as braking and handling performance are one of the most important evaluation items .
In this context, as an additional function of MF-Tyre/MF-Swift, the Magic Formula has been extended based on the physical phenomena occurring between the tire and the wet road surface to capture tire slip characteristics with different speeds and water film thicknesses.

Development of a Method for Predicting Tire Cornering Force Curve Using Machine Learning

YUDAI MIKAMI･HARUYUKI SUZUKI (Sumitomo Rubber Industries)

We developed a method for predicting the cornering force curve of tires using machine learning. Feature selection and the direction of changes for each feature are determined using a linear model, and a GBDT-based model is used for training. During this process, the direction of changes is incorporated into the model as monotonic constraints. This approach allows us to develop a machine learning model that balances prediction accuracy and interpretability.

Improvement of Transient Yaw Response Using Electric Power Steering Considering Tire Characteristics

Daiki Morimoto･Yasunori Seki･Daisuke Yokoi (Suzuki Motor)

Yaw rate response to steering varies with vehicle and tire characteristics, an optimal response speed exists, as confirmed by subjective evaluations. Using a vehicle-steering model that incorporates tire characteristics from actual data, a simulation was conducted to adjust the rise time of the yaw rate's transient response to this optimal value using Electric Power Steering (EPS). This adjustment was found to enhance evaluations in real vehicle tests.