{'fraction_non_alphanumeric': 0.015250544662309368, 'fraction_numerical': 0.002178649237472767, 'mean_word_length': 5.022950819672131, '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}}
Tyre-Road friction Coefficient: Estimation Adaptive System

This paper offers an upgraded method for estimating the magnitude of friction between tyres of a motor vehicle and a road surface while investigating road accidents. The above-mentioned method is based on the resultant data of tyre-and-road interworking field tests in case of emergency braking. Such estimation of the magnitude of friction is to be carried out with a focus on the factors affecting the friction processes in the tyreand-road contact. The most important factors, which are included in the synthesized adaptive system used for friction coefficient estimation, have been defined based on the theoretical analysis of the data of deceleration and braking length of motor vehicles. The study of the existing expert methods used for estimating the level of tyre-and-road engagement and the effect of such level on the motional parameters of a motor vehicle has demonstrated the need for upgrading of the existing approaches. Unlike the existing practices, the friction coefficient estimation adaptive system offered by the authors hereof is a self-trainable system. Such system reduces any simulation uncertainty and the probability of occurrence of Type 1 and Type 2 errors. Such result is achieved owing to the fact that the system takes into account the upgraded design of the present-day brake systems and tyres, as well as the speed of motor vehicles and load of their wheels; the system is also efficient because it makes use of the up-to-date mathematical methods which are able to process raw (initial) data under conditions of stochastic and fuzzy uncertainty. The approach offered hereby has demonstrated its efficiency for motor vehicles belonging to categories М1 and N1 and has proven its potential applicability for other categories of motor vehicles.