SPEED BUMP DETECTION FOR AUTONOMOUS VEHICLES USING SIGNAL-PROCESSING TECHNIQUES

Abstract

Autonomous vehicle (AV) is one of the emerging technologies that have far-reaching applications and implications in smart cities. Among the current challenges of the Smart City, Traffic management is of utmost importance. AV technologies can decrease transportation cost and can be used for efficient management and control of traffic flows. Traffic management strongly depends on the road surface condition. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. Detecting road abnormalities provide safety to human and vehicles. Current researches on speed bump detection are based on using sensors, accelerometer and GPS. This makes them vulnerable to GPS error, network overload, delay and battery draining. To overcome these problems, we propose a novel method for speed bump detection that combines both image and signal processing techniques. The advantage of the proposed approach consists in detecting speed bumps accurately without using any special sensors, hardware, Smartphone and GPS.