## 1 Introduction
The most popular approach is relying on color and brightness, given the assumption that the vehicle's taillights dominantly contain red points and their brightness is relatively higher than the other parts of the image. Specifically, they consider the threshold based on different color spaces, such as HSV [12; 13; 14; 15], Lab [16], YUV [17], grayscale [18], or RGB [19], and use some conventional techniques [12; 13; 15; 16; 17; 18; 19] or machine-learning-based techniques [14] to detect the taillights from the image. However, the previous works often lacked a step to verify whether the detected region is a taillight or just a bright object. Furthermore, most of them have not mentioned labeling and determining the pixel coordinates of each detected taillight region, which is mandatory for V2V positioning. (2) Stereo matching problem: Stereo matching [20] is a well-known problem that has appeared in many computer vision studies. In particular, it involves the task of determining which part of one image corresponds to a particular part of another image. These two images are taken of the same real scene but from different viewpoints. Previous studies mostly resolved the stereo correspondence problem by proposing a dense matcher to compare each small patch between two images for depth estimation or 3D reconstruction [21; 22; 23; 24; 25]. These approaches have two major drawbacks. First, the stereo matching that operates on the full image may generate bad results on low textured or non-Lambertian surfaces. Second, since the regions of interest often just fill in a small part of the image, the dense stereo matching task will possibly lead to high computational complexity, which may affect the performance in a real-time application like V2V positioning. (3) Pairing problem: Regularly, to decide whether the two taillights belong to the same vehicle, the common approach is comparing some relations relevant to their size, color, and position with some particular thresholds [12; 13; 14; 15; 16; 17; 18; 19; 26]. The threshold values are usually chosen empirically but must be reasonable to distinguish between the two taillights. Therefore, these methods lack the flexibility to handle different cases in real situations. Furthermore, many of them are applied for the surveillance camera [13; 16; 17; 19; 26], which has a different perspective from the vehicle-mounted camera used in our positioning system. To solve the aforementioned problems, we propose three methods, which are summarized as a three-fold contribution below:  
* A detection method in which we derive a connected-component analysis algorithm to determine the region and estimate the pixel coordinates of each candidate taillight in the image. In this method, we also provide a rule-based verification phase to reduce the number of outliers in detection results as much as possible. * A stereo matching method that uses a gradient boosted tree classifier for ascertaining which taillight in the left image is the same as that in the right image, while the other part of the image, which is not the target for V2V positioning, is ignored. Thanks to that, we reduce the computational complexity when compared with the conventional stereo matching approaches. * A neural-network-based pairing algorithm that adaptively determines the threshold of the horizontal distance, that of the vertical distance, and the correlation between two arbitrary taillights to decide whether they belong to the same vehicle. It is different from the existing works that set the threshold empirically. Furthermore, an outdoor experiment was conducted to capture the images in a nighttime traffic environment. The obtained images were then processed using our proposed methods, to reveal the performances of these methods in real situations. The remainder of this paper is structured as follows.