A supervised machine learning framework for smart tires

Strano, Salvatore; Terzo, Mario; Tordela, Ciro

doi:10.1109/rtsi50628.2021.9597342

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…The large discrepancy between the performances of the current study and Zhang et al may stem from the lack of consideration of the tire strain data in the current study. Therefore, it would be worthwhile to investigate the effect of the strain gauge data in predicting tire wear if the strain gauge is a viable option [ 32 ]. It should be also noted that adding more sensors in the tire increases the chance of failure, power consumption, and cost of the tire monitoring system.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Kim¹,

Park²,

Kim³

2023

Sensors

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Excessive tire wear can affect vehicle driving safety. While there are various methods for predicting the tire wear amount in real-time, it is unclear which method is the most effective in terms of the difficulty of sensing and prediction accuracy. The current study aims to develop prediction algorithms of tire wear and compare their performances. A finite element tire model was developed and validated against experimental data. Parametric tire rolling simulations were conducted using various driving and tire wear conditions to obtain tire internal accelerations. Machine-learning-based algorithms for tire wear prediction utilizing various sensing options were developed, and their performances were compared. A wheel translational and rotational speed-based (V and ω) method resulted in an average prediction error of 1.2 mm. Utilizing the internal pressure and vertical load of the tire with the V and ω improved the prediction accuracy to 0.34 mm. Acceleration-based methods resulted in an average prediction error of 0.6 mm. An algorithm using both the vehicle and tire information showed the best performance with a prediction error of 0.21 mm. When accounting for sensing cost, the V and ω-based method seems to be promising option. This finding needs to be experimentally verified.

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Section: Discussionmentioning

confidence: 99%

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Kim¹,

Park²,

Kim³

2023

Sensors

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“…In fact, Maurya et al [6] used a small scale two-layer feedforward neural network (NN) with ten neurons in its hidden layer to estimate the tire pressure based on 3D printed strain sensors. Then, Strano et al [9] utilized a physical model [10] for strain-based intelligent tires to generate virtual data for training supervised machine learning methods. This alleviates the data demand of DL methods, although their approach lacked numerical or experimental evaluation.…”

Section: Introductionmentioning

confidence: 99%

Road Type Classification Using Time-Frequency Representations of Tire Sensor Signals

Dózsa,

Jurdana,

Šegota

et al. 2024

IEEE Access

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Smart tire technologies offer a novel sensing methodology for vehicle environment perception by providing direct measurements of tire dynamics parameters. This information can be utilized in advanced driver assistance systems as well as autonomous vehicle control to enhance vehicle performance and safety. Considering these criteria, we develop algorithms for categorizing road types based on tire sensor signals.Road differentiation is a complex task due to the non-linear and non-stationary nature of the measured tire signals. To address this challenge, we fuse time-frequency distributions with machine learning approaches. Maintaining the robustness of the predictions, we integrate our own measurement system into a Nissan Leaf test vehicle and collect data involving diverse environmental factors and operational conditions, mimicking real-world scenarios. We showed that by experiments our predictions strongly correlate with road quality, which can be utilized in automatic vehicle control, such as intelligent speed adaptation.

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Application of Artificial Neural Networks in the Prediction of Tire Manufacturing Defects

Majewski,

Dostatni,

Diakun

et al. 2023

Lecture Notes in Mechanical Engineering

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A supervised machine learning framework for smart tires

Cited by 7 publications

References 21 publications

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information

Road Type Classification Using Time-Frequency Representations of Tire Sensor Signals

Application of Artificial Neural Networks in the Prediction of Tire Manufacturing Defects

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