Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

Abstract. Vehicle gearbox dynamics is characterized by time varying mesh stiffness. The paper presents a survey of methods used for determining mesh stiffness and the analysis of the centre distance influence on it. The refined mathematical transmission model presenting the centre distance as a variable is presented. The centre distance error as well as backlash and bearing flexibility is defined and the influence of these factors on mesh stiffness and spur gear dynamics is investigated. The results obtained from this paper may be used in gear-box diagnostics.

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