Study of Car-following Model Based on Minimum Safety Distance

Xu, Lunhui; Luo, Qiang; Wu, Jianwei; Huang, Yanguo

doi:10.1061/jhtrcq.0000094

Cited by 20 publications

(20 citation statements)

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“…During braking, the deceleration of vehicle changes with time, as shown in Figure 2 [1,30]. Phase I is the reaction phase before performing, and Phase II is the coordination phase between the brake pedal and the vehicle.…”

Section: Analysis Of Vehicle Braking Processmentioning

confidence: 99%

“…e reaction time of each stage is set to t I,II , t III , and t IV ; the driving distances are s I,II , s III , and s IV ; the initial speed of leading vehicle A and following vehicle B is v 1 and v 2 , respectively; and the maximum deceleration is a max . According to the research in the literature [1], the calculation formula of the braking distance of the vehicle during a complete braking process is F i is slightly more important than F k 5 F i is evidently more important than F k 7 F i is considerably more important than F k 9 F i is extremely more important than F k 2, 4, 6, 8…”

Section: Analysis Of Vehicle Braking Processmentioning

confidence: 99%

“…e safe distance should be sufficient for the following vehicle' driver who has sufficient reaction time to slow down when the leading vehicle conducts emergency brake for preventing rear-end collision. Some studies have found that the safety of car following and the required safety following distance are affected by many factors, such as driver characteristics, weather, and road conditions [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Research of Vehicle Rear-End Collision Model considering Multiple Factors

Luo

Zang

Yuan

et al. 2020

Mathematical Problems in Engineering

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e accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. e previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). e two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. e research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.On the basis of the traditional safety distance model, Kong et al. conducted information acquisition, distance judgment, braking, and other processes for active collision avoidance during vehicle travel. However, the judgment model used is relatively simple [5]. Jiang and Yu established a highway antitailing model based on radial basis function neural network by using traditional algorithms [6]. Nevertheless, the model is mainly applied to highways, and the influencing factors are considered to be single. Jing and Wang explored the influence of multiple vehicles on the driver and established a vehicle-following model considering the traffic state based on the full velocity difference (FVD) model [7]. Nonetheless, the model is too complicated to conduct to practical applications. Xia established a fuzzy inference system based on the characteristics (e.g., driving age, and gender) of the driver to determine the response time of different drivers and proposed an improved version of the safe model [8]. Peng and Sun derived the modified Korteweg-de Vries (mKdV) equation to describe the traffic

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Section: Analysis Of Vehicle Braking Processmentioning

confidence: 99%

Section: Analysis Of Vehicle Braking Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research of Vehicle Rear-End Collision Model considering Multiple Factors

Luo

Zang

Yuan

et al. 2020

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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“…In ACC and collisionavoidance systems, the tire-road friction coefficient is used to adjust the braking stance [13]. In the research on carfollowing, only the collision-avoidance model takes into account the real-time road surface condition [14,15]. The theoretical maximum deceleration is used to calculate the safe following distance.…”

Section: Real-time Conditions In Car-following Modelmentioning

confidence: 99%

“…Taking into account that wireless communication is not mature, we assume that max is equal to max ; that is, max = max . In a cut-in scenario, if the minimum desired following distance is calculated by (15), excess adjustment might occur. But the IDM has better performance.…”

Section: Desired Minimum Distance Modificationmentioning

confidence: 99%

The Research of Car-Following Model Based on Real-Time Maximum Deceleration

Yang

Zhang

Gong

et al. 2015

Mathematical Problems in Engineering

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This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.

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Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving

Zhao

et al. 2019

Accident Analysis & Prevention

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Study of Car-following Model Based on Minimum Safety Distance

Cited by 20 publications

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Research of Vehicle Rear-End Collision Model considering Multiple Factors

Research of Vehicle Rear-End Collision Model considering Multiple Factors

The Research of Car-Following Model Based on Real-Time Maximum Deceleration

Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving

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