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Intelligent vehicles can improve traffic safety and reduce damage caused by traffic accidents. Environmental perception system is the core of the intelligent vehicle which detects vehicles and pedestrians around the ego host-vehicle by using vehicle environmental perception sensors. Environmental perception system with the multi-sensor information fusion algorithm can utilize the advantages of each environmental perception sensor and detects targets with higher detection probability and precision. Most of the published papers are based on the sensor level fusion architecture which is not stable and robust in detecting target. This paper presents a multi-sensor fusion algorithm with central level architecture, which can improve the target detection probability compare to these with the sensor level fusion architecture.

Millimeter-wave automotive radars can prevent traffic accidents and save human lives as they can detect vehicles and pedestrians even in night and in bad weather. Various types of automotive radars operating at 24 and 77 GHz bands are developed for various applications, like adaptive cruise control, blind-spot detection and lane change assistance. In each year, millions of millimeter-wave radar are sold worldwide. Millimeter-wave radar is composed of radar hardware and radar signal processing software, which detects the targets among noise, measures the distance, longitudinal speed and the azimuth angle of the targets, tracks the targets continuously, and controls the ego vehicle to brake or accelerate. Performance of the radar signal processing software is closely related with the radar hardware properties and radar measurement conditions.