An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles

Authors:
M. ValdéS-Vela;R. Toledo-Moreo;F. Terroso-SáEnz;M. A. Zamora-Izquierdo
Affiliations:
Department of Information and Communication Engineering, Universidad de Murcia, Facultad de Informática, Campus de Espinardo S/N, 30100 Murcia, Spain;Department of Electronics, Computer Technology and Projects, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;Department of Information and Communication Engineering, Universidad de Murcia, Facultad de Informática, Campus de Espinardo S/N, 30100 Murcia, Spain;Department of Information and Communication Engineering, Universidad de Murcia, Facultad de Informática, Campus de Espinardo S/N, 30100 Murcia, Spain
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

Road traffic collisions are an outstanding problem in current developed societies. This paper presents a solution to support collision avoidance based on the timely detection of the vehicle maneuvers. Since the longitudinal interaction among vehicles, with the commonly known car-following behavior, is one of the most important causes of crashes, it was decided to focus on longitudinal maneuvers, identifying the maneuvering states of cruise, accelerating or decelerating and stop. The classification is carried out by means of fuzzy rules extracted from navigational data. Therefore, in our proposal no extra sensors are needed apart from two commonly installed for navigation purposes: the odometry of the vehicle and an accelerometer. The system was tested with low-cost sensors showing good results when compared to the literature of the field.