Studies from Jiangsu University Yield New Information about Risk Management (A forward collision avoidance algorithm based on driver braking behavior)

2019 JUN 13 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Fresh data on Risk Management are presented in a new report. According to news reporting from Zhenjiang, People’s Republic of China, by NewsRx journalists, research stated, “Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems.”

Financial supporters for this research include National Basic Research Program of China (973 Program), National Natural Science Foundation of ChinaNational Natural Science Foundation of China, Key Research and Development Program of Jiangsu Province, Natural Science Foundation of Jiangsu ProvinceNatural Science Foundation of Jiangsu Province.

The news correspondents obtained a quote from the research from Jiangsu University, “Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia ‘100-car’ database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation.”

According to the news reporters, the research concluded: “Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver’s normal driving compared with other algorithms.”

For more information on this research see: A forward collision avoidance algorithm based on driver braking behavior. Accident Analysis & Prevention, 2019;129():30-43. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

Our news journalists report that additional information may be obtained by contacting Y. Cai, School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, 212013, Zhenjiang, Jiangsu, People’s Republic of China. Additional authors for this research include M. Wang, Y. Cai, L. Chen, H. Farah and M. Hagenzieker.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2019.05.004. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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