Citation

Song W, Yang Y, Fu M, Qiu F, Wang M. IEEE Trans. Intel. Transp. Syst. 2018; 19(3): 758-773.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2017.2700628

PMID

unavailable

Abstract

This paper presents real-time obstacles detection and their status classification method for collision warning in the vehicle active safety system. Specifically, stereo cameras and millimeter wave (mmw)-radar are fused to help the driving ego-vehicle to find "Danger" or "Potential Danger" in a timely way through combining with the vehicle kinematic model. The proposed method makes full use of the unique advantages of stereo cameras and mmw-radar to sense the environment through several modules. Cameras are mainly used to detect the near or lateral dynamic objects and to obtain the obstacles region of interest (ROI) considering its rich information and high sensitivity to the lateral displacement, while far or longitudinal relative dynamic objects are detected by mmw-radar according to its observational ability to make up for the disadvantage of cameras. In detail, a cameras detector utilizes "error vectors" rather than the optical flow to obtain dynamic classes through two times clustering. Mmw-radar mainly detects relative dynamic objects, whose absolute speed can be computed according to the ego-vehicle's state. Then, the detected objects of these two detectors are integrated in an obstacles ROI map, which is obtained through an UV-disparity obstacles detection algorithm to get the final dynamic and relative dynamic objects. Finally, they are classified by comparing them with a dangerous area that is acquired according to the vehicle kinematic model in a special vehicle coordinate system, which is fixed to the ground temporarily. This method is tested on our mobile platforms and the results prove that it can work effectively even though the ego-vehicle drives quickly.

Language: en

Keywords

advanced driver assistance systems; alarm systems; Automobiles; Autonomous vehicle; Cameras; clustering; collision avoidance; collision warning; dangerous area estimation; Dynamics; ego-vehicle; Heuristic algorithms; image classification; intelligent transportation systems; millimeter wave-radar; mmw-radar data; object detection; obstacles detection and status classification; obstacles ROI map; Potential Danger; real-time obstacle detection; Real-time systems; road safety; road vehicles; Safety; status classification method; stereo cameras; stereo image processing; stereo vision; structured road environment; traffic engineering computing; UV-disparity; UV-disparity obstacles detection algorithm; vehicle active safety system; Vehicle dynamics; vehicle kinematic model