Adla, Rawa, Youssef Bazzi, and Nizar Al-Holou
Motor vehicles crashes are the leading cause of death among Americans, there are approximately 1.6 million of rear-end crashes in the U.S. each year. As a result the forward collision avoidance has received intensive efforts in the research and auto industry as a method to save passengers lives. In this work a new sensor fusion algorithm based on quantifying the dependency values (the amount of overlapping) between two sensor’s data is proposed. The hypothesis generated by combining the joint probability value between the two events. The certainty measure of this hypothesis depends on the sensors output. In the classical case the computation of the hypothesis value is performed on the assumption that the two sensors are independent, while in reality these two sensors output affects the certainty measure of the hypothesis.
The proposed method, adjusts the vehicle speed based on a real time data in order to prevent any potential collision. This methodology works by integration of three data sources, speedometer of the host vehicle and two other sensors measure the speed of the vehicle in front (using Laser, Radar, or V2V communication). Our proposed methodology has been simulated using MATLAB and shown a more reliable and less potential accident to occur.