Data-Driven Tire Capacity Estimation With Experimental Verification

Tire states and capacity monitoring is critical for vehicle and wheel stabilization controls in automated driving and active safety systems. Tire capacity, which represents the performance margin of tire forces from its limits, determines the operational range for vehicle control systems and their actuation through steering or torques at each tire to maintain stability while performing trajectory following. This paper presents a generic tire capacity identification framework that can handle different normal loads, road surface friction, and combined-slip driving scenarios, which are challenging for stabilization and tracking control programs in automated driving systems. A novel measuring method for generating force-training data is designed by combining the indoor tire test procedure and tread rubber friction test rig, in order to obtain adequate and high-quality benchmark datasets. The results from large data sets from road experimenting and indoor tire test facilities, including pure- and combined-slip conditions, confirm effectiveness of the developed learning-based tire capacity estimation which utilizes notions from the model description with bounded uncertainty. More importantly, the proposed method can provide reliable tire properties ranging from the linear to the sliding regions. Further validation is performed on a real test car with on-board sensory measurements, and the results confirm accuracy of the proposed method for various free rolling and hard launch/brake scenarios.