{'fraction_non_alphanumeric': 0.014419610670511895, 'fraction_numerical': 0.0, 'mean_word_length': 5.516431924882629, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Strain Measurement-based Self-Diagnosis of Tire Wear Conditions in Slow Driving Vehicles

Tire wear conditions are crucial for safe vehicle control and should be managed properly. The demand for technologies for self-diagnosis of wear conditions is expected to increase to save time and effort required for daily tire checking in fleet management companies or to enable self-notification of the tire replacement requirement in autonomous vehicles. Although several methodologies based on vibration or acceleration analysis have been proven to be feasible, their functionality is critically limited under low-speed conditions. To solve this problem, this study proposes a novel methodology using inner liner strain measurement. We experimentally confirmed that the ratio of the circumstantial strain around the trailing edge to that around the leading edge of a tire decreases according to tire wear. The strain reduction rates in the trailing edge were stable under various speeds, loads, and tire inflation pressure conditions, suggesting that this feature could be a reliable indicator for estimating tire wear conditions under various situations, including slow-driving autonomous vehicles. The mechanism hypothesis for the results is based on the fundamental knowledge of pneumatic tire mechanics, indicating that our observation is generally applicable and not unique to our system.