Author
Abstract
This paper proposes a method of detecting driving vehicles, estimating the distance, and detecting whether the brake lights of the detected vehicles are turned on or not to prevent vehicle collision accidents in highway tunnels. In general, it is difficult to determine whether the front vehicle brake lights are turned on due to various lights installed in a highway tunnel, reflections on the surface of vehicles, movement of high-speed vehicles, and air pollution. Since driving vehicles turn on headlights in highway tunnels, it is difficult to detect whether the vehicle brake lights are on or not through color and brightness change analysis in the brake light area only with a single image. Therefore, there is a need for a method of detecting whether the vehicle brake lights are turned on by using a sustainable change obtained from image sequences and estimated distance information. In the proposed method, a deep convolutional neural network(DCNN) is used to detect vehicles, and inverse perspective mapping is used to estimate the distance. Then, a long short-term memory (LSTM) Network that can analyze temporal continuity information is used to detect whether the brake lights of the detected vehicles are turned on. The proposed method detects whether or not the vehicle brake lights are turned on by learning the long-term dependence of the detected vehicles and the estimated distances in an image sequence. Experiments on the proposed method in highway tunnels show that the detection accuracy of whether the front vehicle brake lights are turned on or not is 90.6%, and collision accidents between vehicles can be prevented in highway tunnels.
Suggested Citation
JongBae Kim, 2022.
"Detecting the Turn on of Vehicle Brake Lights to Prevent Collisions in Highway Tunnels,"
Sustainability, MDPI, vol. 14(21), pages 1-23, November.
Handle:
RePEc:gam:jsusta:v:14:y:2022:i:21:p:14322-:d:960991
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14322-:d:960991. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.