The impact of weather conditions and driver characteristics on road safety on rural roads

Pavlou, Dimosthenis; Christodoulou, Gregory; Yannis, George

doi:10.1016/j.trpro.2023.11.369

Transportation Research Procedia

2023

DOI: 10.1016/j.trpro.2023.11.369

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The impact of weather conditions and driver characteristics on road safety on rural roads

Dimosthenis Pavlou,

Gregory Christodoulou,

George Yannis

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2024

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Cited by 2 publications

References 14 publications

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Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Wei,

Zhu,

Bai

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Weather visibility interference has a significant impact on driver car-following behavior. To investigate drivers’ car-following behavior and emergency avoidance behaviors under different visibility disturbances, scenarios are constructed under different foggy concentration environments based on driving simulation, and the drivers’ response behaviors are collected in the stable car-following state and emergency rear-end scenarios. Exploring the differential effects of gender and driving experience on driving behavior for fog concentrations based on multifactorial analysis of variance. A quantitative model of car-following risk is constructed based on factor analysis, and a linear mixed model is used to explore the comprehensive effects of fog concentration, speed, and the following distance at the braking time on drivers’ braking reaction time by fully considering the differences in individual behaviors. The results show that driving behavior is significantly affected by visual visibility, driver’s gender, and driving experience. With the decrease of visibility, following driving speed decreases, the following distance is shortened, the headway decreases, and the standard deviation of lane lateral offset distance increases. The rear-end collision risk of an experienced driver is higher than that of a novice driver, and the rear-end collision risk of the female is higher than that of male. The risk of collision is higher when traveling in light fog. In emergency rear-end collision scenarios, as visibility decreases, braking reaction time increases, and the risk of collision conflict increases at the moment of driver braking. The braking reaction time of the driver decreases with the increase of the speed and increases with the increase of the distance when the front vehicle is braking. The results of this study provide theoretical support and technical reference for effectively improving driving safety in a bad-visibility environment.

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Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Wei,

Zhu,

Bai

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Trajectory planning for intelligent vehicle obstacle avoidance in rainy and snowy conditions

Zhu,

He,

Liu

2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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To address the problems of intelligent vehicles navigating obstacles on rainy and snowy road surfaces, this study establishes a two-layer model comprising of an upper-level local path planning layer and a lower-level trajectory tracking control layer. The aim is to enhance the safety and stability of intelligent vehicles during lane changes and obstacle avoidance. In the local path planning layer, an obstacle avoidance function is introduced and optimized using the repulsive force function of the artificial potential fields method, followed by analysis using nonlinear model predictive control. In the trajectory tracking control layer, constraints are designed on front-wheel steering angle, steering angle increment, center of mass lateral deviation angle, lateral acceleration, yaw angle, and yaw angular velocity based on model predictive control and two-degree-of-freedom vehicle dynamics constraints. To validate the effectiveness of the model, this study utilized the CarSim/Simulink joint simulation platform to establish three different vehicle speeds of 36, 72, and 108 km/h, and designed two typical experimental scenarios of single obstacle avoidance and multiple obstacle continuous avoidance. The results indicate that optimizing the obstacle avoidance function using the repulsive force function of the artificial potential field method can reduce the lateral displacement of the intelligent vehicle by an average of 0.079 and 0.118 m in both scenarios, resulting in smoother trajectories. Additionally, the yaw angle is reduced by an average of 0.392° and 0.407°, making the vehicle more stable.

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The impact of weather conditions and driver characteristics on road safety on rural roads

Cited by 2 publications

References 14 publications

Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Trajectory planning for intelligent vehicle obstacle avoidance in rainy and snowy conditions

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