Model calibration concerning risk coefficients of driving safety field model

Abstract

Driving safety field (DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver’s hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

Keywords

intelligent connected vehicles / advanced driver assistance systems (ADAS) / driving risk assessment / driving safety field (DSF) model / parameter calibration / grey relation degree

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