Abstract
Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.
Highlights
In the field of automobile research, many studies focus on estimating the type of surface on which vehicles move
The elaborated classification procedure in this paper addresses the task of classifying surfaces from acceleration-sensor data using classification tools based on artificial neural networks (ANNs)
The signal is continuously processed with a frequency of 1 kHz, while the classification system works on predetermined time windows
Summary
In the field of automobile research, many studies focus on estimating the type of surface on which vehicles move. The question is not trivial, since the improvement of the performance of cars, autonomous vehicles, or mobile robots depends on its correct determination. The automotive industry focuses a large part of its efforts on providing vehicles with the latest advances in perception or autonomous decision making in order to improve the performance of their systems and reduce or even the possible effects of an accident. The precise determination of a road surface is fundamental for the improvement of vehicle dynamics. Many systems can be fed with this information, such as the antilock braking system (ABS), traction-control system (TCS), connected vehicles (V2V), and vehicle-to-road infrastructure (V2I). All require the most precise surface characteristics to be able to maximize their potential. Comfort, performance, and traffic management are some of the areas that can be improved with the evolution of these technologies
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