A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

Featured Application This paper proposes taillight detection, stereo taillight matching, and taillight pairing methods for the stereo-vision-based nighttime vehicle-to-vehicle positioning process. This process can be used in advanced driver-assistance systems and autonomous vehicles. The stereo vision system has several potential benefits for delivering advanced autonomous vehicles compared to other existing technologies, such as vehicle-to-vehicle (V2V) positioning. This paper explores a stereo-vision-based nighttime V2V positioning process by detecting vehicle taillights. To address the crucial problems when applying this process to urban traffic, we propose a three-fold contribution as follows. The first contribution is a detection method that aims to label and determine the pixel coordinates of every taillight region from the images. Second, a stereo matching method derived from a gradient boosted tree is proposed to determine which taillight in the left image a taillight in the right image corresponds to. Third, we offer a neural-network-based method to pair every two taillights that belong to the same vehicle. The experiment on the four-lane traffic road was conducted, and the results were used to quantitatively evaluate the performance of each proposed method in real situations.