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The Driving Safety Field Based on Driver–Vehicle–Road Interactions

IEEE Transactions on Intelligent Transportation Systems · 2015 · Vol. 16(4) · pp. 2203–2214
Jianqiang WangJian WuYang Li

Abstract

Vehicle driving safety is influenced by many factors, including drivers, vehicles, and road environments. The interactions among them are quite complex. Consequently, existing methods that evaluate driving safety perform inadequately because they only consider limited factors and their interactions. As such, it is difficult for kinematics-based and dynamics-based vehicle driving safety assistant systems to adapt to increasingly complex traffic environments. In this paper, we propose a new concept, i.e., <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>the driving safety field</i></b> . The concept makes use of field theory to represent risk factors owing to drivers, vehicles, road conditions, and other traffic factors. A unified model of the driving safety field is constructed, which includes the following three parts: 1) a potential field, which is determined by nonmoving objects on the roads, such as a stopped vehicle; 2) a kinetic field, which is determined by the moving objects on roads, such as vehicles and pedestrians; and 3) a behavior field, which is determined by the individual characteristics of drivers. Moreover, the applications of the model are proposed, and its application to a typical car-following scenario is illustrated, which evaluates the risks caused by multiple traffic factors. The driving safety field can reveal driver–vehicle–road interactions and their influences on driving safety, as well as predict driving safety trends owing to dynamic changes. In addition, the model can provide a new foundation for establishing driving safety measures and active vehicle control under complex traffic environments.

Traffic control and managementAutonomous Vehicle Technology and SafetyVehicular Ad Hoc Networks (VANETs)Field (mathematics)Transport engineeringActive safetyAdvanced driver assistance systemsVehicle dynamicsEngineeringPoison controlComputer scienceAutomotive engineeringAerospace engineering

Funding

  • National Natural Science Foundation of China
Citations
407
FWCI
7.83
field-weighted impact
References
42
Percentile
97%
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Citations per year
Cited by
A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles
IEEE Transactions on Intelligent Transportation Systems · 2016 · 637 citations
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