Abstract
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.
Highlights
The automotive industry is facing the challenge of automated driving
Road friction estimation is a useful tool for various aspects in driving safety alerting a driver about the road-surface conditions, modifying vehicle active safety systems thresholds or reporting information to a vehicle or road infrastructure network
The anti-lock braking system (ABS) algorithm proposed in [39] was used in this investigation and it and the control strategy are presented in Appendix A
Summary
The automotive industry is facing the challenge of automated driving. Achievements in fields of vehicle dynamics, control engineering, and artificial intelligence enable implementation of this technology. Road friction estimation is a useful tool for various aspects in driving safety alerting a driver about the road-surface conditions, modifying vehicle active safety systems thresholds or reporting information to a vehicle or road infrastructure network. Wang et al [7] proposed two different approaches for road pavement type and its coating identification using two approaches: (i) effect-based—identifies road friction conditions through estimating dynamic parameter response of the vehicle; (ii) cause-based—. The main advantage of the cause-based approach is road friction conditions identification before reaching the measured surface point. Niskanen and Tuononen [13] proposed the friction identification by estimating a three-axis accelerometer mounted inside the tyre While such a method can be applied to detect friction potential indicators, different levels of pavement roughness still cause undesirable vibration and a negative influence on results. The investigation of created system efficiency was provided using MM of a vehicle, tyres and ABS, MMs were validated using experimental data
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