Abstract
Aiming at the driving safety problem on the curve, safety speed warning system with risk prediction is designed as the vehicle drives on the curve. Considering both the static multi-factor such as drivers, vehicles, roads and vehicle dynamics performance, safety speed warning model on the curve based on multiple sources is established. The road curvature is estimated by machine vision technology. Then, the maximum safety speed is computed. Also, the risk state estimation function is introduced to judge vehicle driving state in advance. And, the alarm criteria is determined. Finally, safety speed warning system is developed with LabVIEW. The simulation results shown that it can accurate predict the driving state of the vehicle on the curve and warn for drivers when the vehicles have a safety hidden.
References
Farmer CM, Lund AK (2002) Rollover risk of cars and light trucks after counting for driver and environmental factors. Accid Anal Prev 34(11):163–173
Parenteaua CS, Vianob DC, Shaha M (2003) Field relevance of a suite of rollover tests to real world crashes and injuries. Accid Anal Prev 35(3):103–110
Li K, Tan HS, Misener JA et al (2008) Digital map as a virtual sensor-dynamic road curve reconstruction for a curve speed assistant. Veh Syst Dyn 46(12):1141–1158
Lee YH, Deng WW Speed control method for vehicle approaching and traveling on a curve: U.S., US400963B2. 15 July 2008
Zhang D, Wang J, Li S et al (2009) Risk prediction based curve anti-sideslip speed warning system. J Highw Transp Res Dev 26(S1):44–48
Yu G, Li Q, Wang Y et al (2014) Roll stability and early warning of vehicle driving in the curve. J Beijing Univ Technol 40(4):574–579
Wang C (2010) Study on road traffic safety analysis and countermeasures in curve. Chang’an University, Xi’an
Zhou H (2012) Simulation research of vehicle safe speed based on car and road conditions. Chongqing Jiaotong University, Chongqing
Li B, Cao Y, Li Z et al (2014) Turn safety of concrete truck mixer based on LabVIEW. J Chognqing Jiaotong Univ 33(1):144–147
Wang C (2012) Research on several key problems of vehicle lane change warning, vol 6. Chang’an University, Xi’an, pp 69–77
Zheng S, Li X, Li K et al (2007) Simulation of vehicle active steering based on digital map information. J Highw Transp Res Dev 24(11):154–158
Swami A, Zhao Q, Hong Y-W (2003) Wireless sensor networks: signal processing and communications, vol 2, pp 1105–1108
Jia L, Luo J (2012) Road curvature estimation based on linear lane model. J Jiangsu Univ Nat Sci Ed 33(4):374–378
Xie M (2011) Research on active safety technology of vehicle based on monocular vision, vol 5. University of Electronic Science and Technology of China, Chengdu, pp 16–20
Southall B, Taylor CJ (2001) Stochastic road shape estimation. In: Proceedings of eighth IEEE international conference on computer vision, vol 1, pp 205–212
Zhao SE, Qu X, Shu HB et al (2004) Mountain road traffic safety evaluation based on weighted least squares. J Math Prac 44(13):88–90
Acknowledgements
Authors would like to thank anonymous reviewers and the editor for their valuable comments and Municipal Education Commission Project of Chongqing (KJ1603207). Chongqing Vocational Institute of Engineering Research Project (KJB201511) for their sponsorship.
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Qu, X., Yu, F., Zhao, Se. (2017). Research on Curve Safety Speed Warning for Vehicle with Risk Prediction. In: Proceedings of SAE-China Congress 2016: Selected Papers. SAE-China 2016. Lecture Notes in Electrical Engineering, vol 418. Springer, Singapore. https://doi.org/10.1007/978-981-10-3527-2_37
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DOI: https://doi.org/10.1007/978-981-10-3527-2_37
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