This paper focuses on a night time driving performance evaluation of an image-based lane departure warning (LDW) system which was previously developed by the author [25, 26]. In the previous study, road testing was only conducted under the day time driving condition. So, the main focus here is to conduct night time driving evaluations. This system uses the Lucas-Kanada (L-K) optical flow and the Hough transform methods approach. Based on the status of the vehicle deviating from its heading lane, the method integrates both techniques to establish an operation algorithm to determine whether a warning signal should be issued. The L-K optical flow tracking is used when the lane boundaries cannot be detected, while the lane detection technique is used when they become available. Even though both techniques are used in the system, only one method is activated at any given time because each technique has its own advantages and also disadvantages. The image-based LDW system was road tested on rural highways and this paper briefly presents our night time driving test results.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Feb 5 2018|
|Event||12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017 - Siem Reap, Cambodia|
Duration: Jun 18 2017 → Jun 20 2017
|Name||Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017|
|Other||12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017|
|Period||6/18/17 → 6/20/17|
Bibliographical noteFunding Information:
* Resrach supported by ITS Institute, University of Minnesota’s Center for Transportation Studies (CTS). Financial support was provided by the US Department of Transportation (USDOT)’s Research and Innovative Technologies Administration (RITA).
ACKNOWLEDGMENT The research was funded by the Intelligent Transportation Systems (ITS) Institute, a program of the University of Minnesota’s Center for Transportation Studies (CTS). Financial support was provided by the United States Department of Transportation (USDOT)’s Research and Innovative Technologies Administration (RITA). The project was also supported by the Northland Advanced Transportation Systems Research Laboratories (NATSRL), a cooperative research program of the Minnesota Department of Transportation (MnDOT) and the ITS Institute. The author would also like to thank Gregory Taubel and Rohit