Implementation of a fuzzy-inference-based, low-speed, close-range collision-warning system for urban areas

Abstract

Traffic accidents are still increasing even though vehicles are becoming more intelligent to enhance driver convenience and safety. Single car-on-car rear impacts in urban areas have increased rapidly due to driver inattention. According to a Road Traffic Authority (ROTA) report in Korea in 2006, 85.2% of single car-on-car rear impact accidents occurred at less than 60 km/h, and 25.3% of the total occurred at between 30 km/h and 50 km/h. To prevent rear vehicle crashes in urban areas, automobile manufacturers have developed various low-speed, close-range collision-warning systems. This paper presents a low-speed, close-range collision-warning algorithm for urban areas using fuzzy inference. Experiments using an embedded microprocessor in the driving track demonstrated the feasibility of the proposed collision-warning system. The results indicate that the fuzzy inference-based, low-speed, close-range collision-warning system could reduce traffic accidents by alerting the driver to potential collisions.