Stability (learning theory)

Identification (biology)

Automotive engineering

Electronic stability control

Road surface

Biology

Advanced driver assistance systems

Braking distance

Machine learning

Civil engineering

Engineering

Artificial intelligence

Systems engineering

Computer science

Task (project management)

Botany

Brake

Vehicle dynamics

Braking system

Simulation

Artificial neural network

Anti-lock braking system

Sensors

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.

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation