This is an article about the lane dection of cars work
Key Points
-Region of interest
-editing the photo
Citation
@article{10.1145/2522968.2522970,
author = {Yenikaya, Sibel and Yenikaya, G\"{o}khan and D\"{u}ven, Ekrem},
title = {Keeping the Vehicle on the Road: A Survey on on-Road Lane Detection Systems},
year = {2013},
issue_date = {October 2013},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {46},
number = {1},
issn = {0360-0300},
url = {https://doi.org/10.1145/2522968.2522970},
doi = {10.1145/2522968.2522970},
abstract = {The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.},
journal = {ACM Comput. Surv.},
month = jul,
articleno = {2},
numpages = {43},
keywords = {Computer vision, road following, lane detection, intelligent vehicles, road detecetion}
}
Title
Keeping the vehicle on the road: A survey on on-road lane detection systems
URL
https://dl.acm.org/doi/10.1145/2522968.2522970
Summary
This is an article about the lane dection of cars work
Key Points
-Region of interest -editing the photo
Citation
@article{10.1145/2522968.2522970, author = {Yenikaya, Sibel and Yenikaya, G\"{o}khan and D\"{u}ven, Ekrem}, title = {Keeping the Vehicle on the Road: A Survey on on-Road Lane Detection Systems}, year = {2013}, issue_date = {October 2013}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {46}, number = {1}, issn = {0360-0300}, url = {https://doi.org/10.1145/2522968.2522970}, doi = {10.1145/2522968.2522970}, abstract = {The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.}, journal = {ACM Comput. Surv.}, month = jul, articleno = {2}, numpages = {43}, keywords = {Computer vision, road following, lane detection, intelligent vehicles, road detecetion} }
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