Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System

• Published in: IEEE transactions on intelligent transportation systems 2018-03, Vol.19 (3), p.758-773
• Main Authors: Song, Wenjie, Yang, Yi, Fu, Mengyin, Qiu, Fan, Wang, Meiling
• Format: Article
• Language: English
• Subjects: Automobiles; Autonomous vehicle; Barriers; Cameras; Classification; Clustering; Collision avoidance; Collision dynamics; collision warning; dangerous area estimation; Dynamics; Error detection; Hazards; Heuristic algorithms; Kinematics; Lateral displacement; Millimeter waves; Object recognition; Obstacle avoidance; obstacles detection and status classification; Optical flow (image analysis); Radar; Real time; Real-time systems; Safety; stereo vision; UV-disparity; Vehicle dynamics
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2700628

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.