Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion

Abstract

Active research into vehicle detection and tracking using a vision sensor are done for driver assistance systems (DAS) - collision warning and avoidance, vision enhancement, etc. The vehicle detection and tracking algorithm for DAS requires a robust feature extraction and tracking method regardless of the light and road conditions and an exact estimation of vehicle position and velocity regardless of the distance from the ego-vehicle. But most research was carried out in the day time with good lighting conditions and the little research done so far in the night time assumed no interference of headlights from other vehicles. This paper proposes a new robust vehicle detection and tracking method regardless of the light and road conditions at any distance using vision and sonar sensors. We use the sonar sensor for detection and distance estimation within 10 m and the image sensor over 10 m. First, this paper proposes a simple method that can determine the light condition by observing several images and this light condition is used by selecting one of several detection methods. The proposed vehicle detection method in the day time image can extract the shadow region represented by the boundary between a vehicle and the road and further verify using other vehicle features, such as symmetry rate, vertical edge, and lane information. The vehicle tracking method in the day time uses online template matching using the mean image created by several consecutive detection results. The vehicle detection method in the night time extracts bright regions caused by the headlights, taillights, brake lights, etc. and these candidates are verified by observing several consecutive frames.