Abstract
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.
Highlights
Transportation is an essential factor in human lives, as it plays an important role in developing civilization
V2V positioning is a fundamental part of any intelligent transport system, such as an autonomous vehicle, because it helps to prevent traffic accidents and congestion, especially in nighttime situations
The stereo vision system is promising as a potential solution, owing to its existing advantages compared to other existing technologies
Summary
Transportation is an essential factor in human lives, as it plays an important role in developing civilization. Recent work on both scientific and industrial sides has focused on constructing an intelligent transport system (ITS), in which autonomous vehicles are arguably the most attractive part [1,2,3]. It is partly due to the rapid development of artificial intelligent approaches that their contributions have been shown in several fields in recent years [4,5,6]. Since the final target is completely replacing the traditional vehicles with the autonomous ones, there are still many challenges ahead that need to be resolved. The on-road autonomous vehicles may need to estimate each other’s positions to maintain safe distances and make urgent decisions when necessary. There is an increasing demand for an efficient vehicle-to-vehicle (V2V) positioning solution
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