TY - GEN
T1 - TireEye
T2 - 2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022
AU - Huber, Sebastian
AU - Preindl, Peter
AU - Betz, Johannes
N1 - Publisher Copyright:
© 2022 Prognostics and Health Management Society. All rights reserved.
PY - 2022/10/28
Y1 - 2022/10/28
N2 - Automotive tire tread depth significantly influences a car’s safety and must therefore be closely monitored. However, there is currently no on-board solution that can measure tire wear with an error of less than 0.6 mm in real-world conditions. This corresponds to 37.5 % of the mandatory minimum tread depth in most countries. In this paper we present the concept of TireEye, which is an optical device mounted inside the wheel well and facing the road. This device records the cross-section of the longitudinal tread groove and extracts its outline by using adaptive canny edge detection. The tread wear indicators serve to calibrate the scale since they provide the smallest allowed tread depth. We validate this technology with several tires, various lighting conditions, and different road surfaces. It provides a mean absolute error of 0.57 mm in real-world conditions, which outperforms all other on-board tire wear detection methods displayed in state-of-the-art. Although the results are very promising, the hardware costs and the susceptibility to dirt might make it difficult for automotive companies to deploy. This can be counteracted with additional use cases like tire pressure estimation, tire damage detection, and road friction coefficient estimation.
AB - Automotive tire tread depth significantly influences a car’s safety and must therefore be closely monitored. However, there is currently no on-board solution that can measure tire wear with an error of less than 0.6 mm in real-world conditions. This corresponds to 37.5 % of the mandatory minimum tread depth in most countries. In this paper we present the concept of TireEye, which is an optical device mounted inside the wheel well and facing the road. This device records the cross-section of the longitudinal tread groove and extracts its outline by using adaptive canny edge detection. The tread wear indicators serve to calibrate the scale since they provide the smallest allowed tread depth. We validate this technology with several tires, various lighting conditions, and different road surfaces. It provides a mean absolute error of 0.57 mm in real-world conditions, which outperforms all other on-board tire wear detection methods displayed in state-of-the-art. Although the results are very promising, the hardware costs and the susceptibility to dirt might make it difficult for automotive companies to deploy. This can be counteracted with additional use cases like tire pressure estimation, tire damage detection, and road friction coefficient estimation.
UR - https://www.scopus.com/pages/publications/85150455942
U2 - 10.36001/phmconf.2022.v14i1.3242
DO - 10.36001/phmconf.2022.v14i1.3242
M3 - Conference contribution
AN - SCOPUS:85150455942
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Kulkarni, Chetan
A2 - Saxena, Abhinav
PB - Prognostics and Health Management Society
Y2 - 31 October 2022 through 4 November 2022
ER -