Abstract
The effective assessment of drivers' driving capability under the condition of an advanced driver assistance system is of great significance to the precise switching of driving rights between human and machine and the promotion of the development of man-computer collaboration. In this study, real-time collected warning data from bus driver state monitoring system (DSMS) and advanced driver assistance system (ADAS) were utilized to determine the drivers' comprehensive driving capability indicators. The information utility and interaction of the indicators were considered, and an integrated weight method based on standard deviations was proposed. This method was used to combine the entropy weight method and improved analytic network process (ANP), to evaluate the drivers' comprehensive driving capability under man-computer cooperative driving conditions in real time. The results show that the entropy weight method and improved ANP algorithm have good consistency and are significantly correlated and that the integrated weight method is effective and dependable. The top four indicators in the integrated weighting results were eye closure (0.241), yawn (0.210), rapid deceleration (0.186), and lane departure (0.159). Drivers' comprehensive driving capability scores were concentrated in the score range of 1 to 6, with the lowest scores in zones A and B for stages 2, 11 and 21. Therefore, it is necessary to further explore the relationship between driver behavior, vehicle status and road traffic environment within the score range of 1 to 6 so that the man-computer interaction can be optimized and the driver's comprehensive driving capability can be improved.
Highlights
With the rapid development of computer technology, Internet technology, communication technology and artificial intelligence, intelligent vehicles based on electrification, intelligence and network connectivity have become a major trend in the automotive industry
The weight radar graph showed that the integrated weight closed region basically covered the weight closed region of the entropy weight method and improved analytic network process (ANP) algorithm, which verified the effectiveness of the integrated weight method
A comprehensive driving capability evaluation method for man-computer cooperative driving was proposed in this paper
Summary
With the rapid development of computer technology, Internet technology, communication technology and artificial intelligence, intelligent vehicles based on electrification, intelligence and network connectivity have become a major trend in the automotive industry. According to the development process of intelligent and automated vehicles, the U.S Department of Transportation, SAE, etc. The associate editor coordinating the review of this manuscript and approving it for publication was Yue Cao. development of the intelligent vehicle into six levels: no automation, driver assistance, partial automation, conditional automation, high automation, and full automation. Different levels and functions of intelligent vehicle technology are developing rapidly, a fully working automatic driving vehicle has been difficult to achieve in the short term [2]. We have only entered the initial stage of man-computer cooperative driving in which drivers and automatic driving systems collaborate with each other [3], and intelligent vehicles from L1 to L4 levels have to face man-computer cooperative control problems.
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