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.

The paper analyses the impact of the road micro-profile on the duration and the type of the vehicle wheel contact with the road surface driving at different speed. The selected vehicle bicycle model describes vertical displacements of front and rear wheels and their suspension as well as the impact of the vehicle body motion and longitudinal oscillation. International Roughness Index (IRI) and micro-profile irregularities of the road section analysed in the paper were identified using specialized road testing equipment. The experimental investigations measuring the vehicle suspension displacement and the body acceleration were carried out. Frequency characteristics of suspension motion and regularities of vertical movement of the wheel were identified after dividing the investigated road section according to driving modes. The analysis into the wheel contact with the road surface and identified correlations enable to determine the vehicle stability on selected quality roads.

This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.

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