Braking behavior of major approach turning vehicles at rural two-way stop controlled intersections: A naturalistic driving study

Thapa, Raju; Hallmark, Shauna; Oneyear, Nicole

doi:10.1080/15389588.2020.1751831

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…As can be seen from the above figures that there is an obvious correlation between approach speed and reaction distance, with the greater the approach speed, the longer the reaction distance, which is consistent with the results of previous studies [8,19].…”

Section: Discussion On Reaction Characteristicssupporting

confidence: 92%

“…Sato and Akamatsu [7] studied the reaction distance of right-turning drivers when following a vehicle, and the results showed that the relative distance between the two vehicles and speed influenced the position and timing of the drivers operating pedals. apa et al [8] studied the reaction location of turning drivers at stop-controlled intersections, high speed, sidewalk markings, wet roads, and nighttime driving all caused drivers to react further from the intersection. Oneyear et al [9] established the braking behavior model at rural intersections to determine the factors affecting reaction position.…”

Section: Introductionmentioning

confidence: 99%

“…Some key parameters were mostly obtained through modeling or indirect calculations. For example, apa et al [8,20] built segmented linear regression models based on the time-series data of speed and detected reaction position according to the change in slope and the change in the variance of the fitted linear segments. Berndt et al [10] estimated the vehicle position using a laser scanner and Kalman filter and estimated the speed by smooth spline interpolation.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Normal Stopping Behavior of Drivers at Urban Intersections in China

Yuan

Liu

Zhao

et al. 2022

Journal of Advanced Transportation

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Driving behavior analysis plays an important role in improving the human-like driving capability of the Advanced Driver Assistance System (ADAS) and autonomous driving technologies. In this study, 906 data of drivers stopping normally at urban intersections were extracted from natural driving data to study reaction characteristics and braking characteristics of drivers in real road traffic. The effects of traffic state and traffic density on approach speed and reaction distance were studied using one-way ANOVA, and the relationships between maximum deceleration, average deceleration, braking duration, and influencing factors were analyzed using hierarchical multiple regression. The main results indicate the following: (1) compare with free driving, the approach speed in the following state was lower, and following traffic also caused drivers with higher speeds to further increase reaction distance in high-density traffic. (2) Traffic density only affected the approach speed in the free-driving condition. High-density traffic caused drivers to reduce speed. (3) There was a clear correlation between approach speed and reaction distance: the greater the approach speed, the longer the reaction distance. (4) The braking characteristics were mainly affected by the self-vehicle motion state. Drivers braked to a stop with greater average deceleration and maximum deceleration in a shorter time when the approach speed was higher or the reaction distance was shorter. (5) Both traffic state and traffic density had an influence on the braking characteristics. Drivers reduced average deceleration and increased braking duration in the following state and high-density traffic. (6) When following a preceding vehicle to stop, the braking characteristics of drivers were no longer influenced by the traffic density but were related to the relative motion state with the preceding vehicle. In addition to the driving behavior analysis, the identification method of traffic participants and ranging method used in this study will also advance the development of autonomous driving technologies and driver assistance systems.

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Section: Discussion On Reaction Characteristicssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Normal Stopping Behavior of Drivers at Urban Intersections in China

Yuan

Liu

Zhao

et al. 2022

Journal of Advanced Transportation

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“…The correct operation of the drivers can avoid conflict between the vehicle and other traffic participants; therefore, some researchers have analyzed the speed control behaviour of drivers before entering the intersection, which includes the reaction position. Existing studies have shown that driving at night and on wet roads causes drivers to increase their reaction distance [39]. Road markings and flashing lights also cause drivers to start decelerating and braking further from intersections [32].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Gates et al [16] calculated the approach speed and deceleration indirectly based on the distance and time information obtained from videos. Thapa et al [39] developed a piecewise linear regression model to detect driver reaction points based on time-series speed data. These methods inevitably affect the results.…”

Section: Introductionmentioning

confidence: 99%

Speed behaviour of right‐turn drivers at signalized intersections in China

Yuan

Zhao

Liu

et al. 2022

IET Intelligent Trans Sys

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Ensuring human-like driving performance is fundamental to the widespread adoption and acceptance of intelligent driving systems. The speed behaviour of right-turn drivers at signalized intersections was studied to improve the human-like driving capability. The study was conducted using natural driving data and 545 data for drivers turning right at signalized intersections were extracted. YOLOv4 was used to identify the different types of road users. A one-way ANOVA was used to study the influence of the scene complexity, number of lanes, and intersection shape on the speed selection behaviour. Linear regression and binary logistic regression analyses were used to study the influence of road users, number of lanes, intersection shape, and vehicle motion state on the speed control behaviour. The main results indicate the following: (1) Drivers drove slower when there were more traffic participants or fewer lanes. (2) The intersection shape did not have any effect on the speed behaviour of right-turn drivers, either speed selection behaviour or speed control behaviour. (3) The reaction distance was affected only by the approach speed in this study; the greater the speed, the longer the reaction distance. (4) In addition to the vehicle motion state, external environmental factors also play a critical role in the braking characteristics, that is, pedestrians, cyclists, lateral traffic, and number of lanes after the turn.(5) The driver's intention to brake after entering the intersection stemmed from road users who were in collision conflict with the host vehicle and fewer lanes after the turn, rather than the speed at the stop line. This research has a guiding significance for the design of human-like driving of right-turn driving assistance systems. The target classification method and the measurement method of the driving environment complexity also help improve the human-like perception of the right-turn driving assistance system.

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