Tread Depth Monitoring for Automotive Tires

Matthias Kretschmann･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･Willem Versteden (Siemens Industry Software and Services)

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.

機械学習を用いたタイヤのコーナリングフォースカーブ予測手法の構築

三上　裕大・鈴木　晴之（住友ゴム工業）

機械学習を用いたタイヤのコーナリングフォースカーブ予測手法を構築した．線形モデルで特徴量選択と各特徴量の変化の方向を決定し，GBDT系のモデルで学習を行う．その際，変化の方向を単調制約としてモデルに付与する．この手法により予測精度と解釈性を両立する機械学習モデルを構築した．

タイヤ特性を考慮した電動パワーステアリングによる過渡ヨー応答の改善

森元　大樹・関　泰典・横井　大亮（スズキ）

操舵に対するヨー角速度の特性は車両諸元やタイヤ特性によって変化するが，その応答速さには適値があることを主観評価で確認した．実測データに基づくタイヤ特性を考慮した車両－操舵系モデルにてEPSによりヨー角速度の過渡応答の立上がり時間を適値に調整するシミュレーションを行い，実車で評価が高くなることを確認した．