Visibility Detection Based on the Recognition of the Preceding Vehicle’s Taillight Signals

Abstract

This paper proposes a method for visibility detection based on the recognition of the preceding vehicle's taillight signals using in-vehicle camera and millimeter-wave (mm-W) radar. First, we design two methods of vehicle identification. One is to use Haar-like features and an AdaBoost algorithm to train the vehicle classifier, which is mainly used to identify vehicles without turning on the taillights. The other is to identify vehicles with taillights on by means of taillight segmentation. The two identification methods are combined with a Discriminative Scale Space Tracker (DSST) to track the vehicle in the image acquired by vehicle camera and to measure anthropic visibility with mm-W radar. In addition, we drove a test vehicle on a foggy highway and collected experimental data through in-vehicle camera and mm-W radar. The experimenter observed the movement of the vehicle in front until it disappeared from the field of vision and recorded the distance of the vehicle in front measured by radar at that time as human visibility, which was also used as the ground truth to verify the accuracy of the proposed visibility detection method. The experimental results show that the visibility measured by the proposed algorithm is essentially consistent with the visibility obtained by human eyes, that is, the visibility of vehicles with no taillights, clearance lamp, emergency flasher, or fog lamp tends to rise, with an average accuracy of 88%, 91%, 90%, and 95%, respectively. In contrast to the traditional visibility measurement, this method mainly measures the maximum distance that the driver can observe when the front vehicle is not turned on or different taillights are turned on.

Keywords

• Visibility detection
• sensor fusion
• foggy highway
• adaboost algorithm
• target tracking
• taillight signals