A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data

Wang, Xiaoyuan; Chen, Longfei; Shi, Huili; Han, Junyan; Wang, Gang; Wang, Quanzheng; Zhong, Fusheng; Li, Hao

doi:10.3390/s22134883

Cited by 2 publications

(2 citation statements)

References 42 publications

(52 reference statements)

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“…Among the various driving behavior variables derived from the driving simulator experiment, driving speed, acceleration/deceleration, and intervehicle distance gap were used as indicators to confrm driving behavior characteristics. It was confrmed that those were used as major indicators for evaluating driving behaviors in the existing literature [4][5][6][7]. Te data were derived from the driving log data recorded when the simulation program was running.…”

Section: Investigation Of Drivingmentioning

confidence: 99%

“…Driving propensities refer to temperament or approach that favors psychological decisions and behaviors based on the driver's attitude and judgment in actual trafc situations [7]. Numerous studies have investigated the relationship between specifc driving behaviors, including individual driver propensity and engagement in aggressive or dangerous driving behaviors [7][8][9][10][11][12]. In these studies, propensities were defned by assessing individual personality traits or categorizing driving behaviors recognized by individuals, such as their typical driving habits.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Preferred Automated Driving Patterns Based on a Driving Propensity Using Fuzzy Inference System

Hwang,

Lee

2024

Journal of Advanced Transportation

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With the rapid advancements in automated driving technologies, there is a growing demand for the commercialization of advanced automated vehicles. Through these technologies, we envision enjoying various types of entertainment in automated vehicles, apart from manual driving. To achieve widespread acceptance of automated driving, appropriated interactions between users and automated driving systems must occur. From users’ perspective, automated driving vehicle must be operated within users’ comfort, safe, and satisfying perception based on their personal driving style such as aggressive and defensive driving. Thus, during the motion planning phase of automated driving, consideration should be given to the implementation of a behavioral algorithm based on user propensity. However, user preferences for automated driving patterns exhibit considerable variation, making it essential to conduct an in-depth investigation into the preferred automated driving patterns corresponding to users’ propensity. In this study, we confirmed that the characteristics of preferred automated driving patterns can be deduced from comprehensive driving propensities, which were derived by combining inherent driving propensities with simulator-based driving behavior characteristics using the fuzzy logic method. This study confirmed that in the era of automated driving, the preferred automated driving patterns may vary depending on the propensity from the user’s perspective. Considering these differences, it is meaningful in which it suggests the need for automated driving motions to be implemented based on individual preferences that appear according to human factors such as user propensity.

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Section: Investigation Of Drivingmentioning

confidence: 99%