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 ba...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.展开更多
The risk of drinking water is greatly concerned because of the large amount of pesticide applied to paddy field and the contamination of drinking water sources due to the runoff. A mathematical model is developed, ba...The risk of drinking water is greatly concerned because of the large amount of pesticide applied to paddy field and the contamination of drinking water sources due to the runoff. A mathematical model is developed, based on the mass balance, to predict the fate of paddy field pesticides from application, runoff and mixing in a river, taking account of the physical chemical properties and processes of volatilization, degradation, adsorption and desorption. The model is applied to a river basin in Japan to estimate the contaminant level of several popularly used pesticides at the water intakes. The health risk in drinking water induced by each pesticide concerned is estimated and evaluated by comparing with the acceptable daily intake values(ADI) and with that induced by trihalomethanes. An index to evaluate the total risk of all pesticides appearing in water is proposed. The methods for risk management are also discussed.展开更多
基金Projects(51475254,51625503)supported by the National Natural Science Foundation of ChinaProject(MCM20150302)supported by the Joint Project of Tsinghua and China Mobile,ChinaProject supported by the joint Project of Tsinghua and Daimler Greater China Ltd.,Beijing,China
文摘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.
文摘The risk of drinking water is greatly concerned because of the large amount of pesticide applied to paddy field and the contamination of drinking water sources due to the runoff. A mathematical model is developed, based on the mass balance, to predict the fate of paddy field pesticides from application, runoff and mixing in a river, taking account of the physical chemical properties and processes of volatilization, degradation, adsorption and desorption. The model is applied to a river basin in Japan to estimate the contaminant level of several popularly used pesticides at the water intakes. The health risk in drinking water induced by each pesticide concerned is estimated and evaluated by comparing with the acceptable daily intake values(ADI) and with that induced by trihalomethanes. An index to evaluate the total risk of all pesticides appearing in water is proposed. The methods for risk management are also discussed.