DocumentCode :
578368
Title :
On the quantitative assessment of the Lane Departure Warning System based on road scenes simulator
Author :
Wang, Yan ; Fu, Jing ; An, Xiang-jing ; Li, Jian ; Shang, Er-ke
Author_Institution :
Coll. of Mechatron. & Autom., Nat. Univ. of Defense Technol., Changsha, China
Volume :
3
fYear :
2012
fDate :
15-17 July 2012
Firstpage :
941
Lastpage :
948
Abstract :
Vision-based Lane Departure Warning Systems (LDWSs) have been studied for over two decays. This paper presents an Objective Evaluation Platform of LDWS (OEP-LDWS). It provides simulated road scenes with the possible data as ground truth, such as the vehicle to road relation, vehicle´s states and the real Time-to-Lane-Crossing (TLC) value. In our OEP-LDWS, different kinds of driving maneuver can be simulated with the road model, the vehicle model, the camera model and a vehicle trajectory generator. At the same time, the road scene that may be captured by the on board camera can be generated. Using our OEP-LDWS, one can not only evaluate the warning performance of the LDWS quantitatively, but also assess the whole performance under varying circumstance, such as different road surfaces, different road curvatures and so on. Actually, those assessments can hardly be evaluated through real driving test, and are very important aspects for a LDWS, such as warning strategy selection, system tailor. Using our OEP-LDWS, we assess our LDWS with three different warning strategies, and reach the conclusion that the PTLC is the best under the low false warning criterion.
Keywords :
alarm systems; computer vision; road traffic; traffic engineering computing; OEP-LDWS; PTLC; camera model; driving maneuver simulation; low false warning criterion; objective evaluation platform; performance assessment; possible time-to-lane-crossing; quantitative assessment; real time-to-lane-crossing value; road scenes simulator; simulated road scene; vehicle to road relation; vehicle trajectory generator; vision-based lane departure warning system; warning strategy selection; Abstracts; Cameras; Optical sensors; Vehicles; Assessment; Lane departure Warning system; Simulated road scenes; Warning strategy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
ISSN :
2160-133X
Print_ISBN :
978-1-4673-1484-8
Type :
conf
DOI :
10.1109/ICMLC.2012.6359480
Filename :
6359480
Link To Document :
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