Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars

Chen, Yuan-Lin; Wang, Chong-An

doi:10.1109/vetecs.2007.529

Cited by 45 publications

(31 citation statements)

References 3 publications

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“…The calculation of the safety distance is based on a combination between the secure stopping distance (W/(2 * G * ρ * A f * Cd)) * ln (1 + ((ρ * A f * Cd)/2 * (V x (T + 1))2)/((η * µ * W) + (fr * W * cosθ) + (W * sinθ))) and the distance of reaction (T R + T D + T A ) * V x (T + 1). This objective function based on the safety distance defined by Chen and Wang [5]. The first objective function contains a single variable "V x (T + 1)" that represents the velocity, which will be adopted during the next simulation step T + 1.…”

Section: Bi-level Bi-objective Modelingmentioning

confidence: 99%

“…Indeed, while we used the Tabu search as a strategy for decision-making by researching the best solution that answers to the needs of the following driver agent, then we chose a random search time between 0.15 and 0.25 s, which is the stopping criterion to select the best realizable solution. This interval represents the decision time for an ordinary behavior driver [5]. Figure 2 presents the application structure of the Tabu search algorithm.…”

Section: Resolution Based On the Tabu Search Algorithmmentioning

confidence: 99%

“…Firstly, all these research works did not treat the driver behaviors (normative or non normative) during their modeling, which influences considerably on the reality of the simulation, since decision-making differs in these two types of behaviors. Secondly, according to [5], the calculation of the safety distance is based on many factors, which are: the reaction time, the decision time, the action time, the weight of the vehicle, the speed of gravity, the air density, the projection area, the air resistance factor, the efficiency of braking, the friction coefficient, the decay factor and the slope of the road. However, excepting the reaction time, all of the above mentioned factors are not used during the calculation of the safety distance in the research works of [6,11,14].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, these research works used a fixed reaction time, which is far from the reality, because the duration of the reaction time is influenced by the driver behaviors. For example, the duration of reaction time for a common driver is 0.6-0.9 s [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Anticipation Based on a Bi-Level Bi-Objective Modeling for the Decision-Making in the Car-Following Behavior

Bennajeh¹,

Kebair²,

Saïd³

et al. 2016

Intelligent Decision Technologies 2016

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Safety distance models are in a continuous state of improvement due to their important role in the micro-simulation of traffic, intelligent transport systems and safety engineering models. Indeed, many existing models of car-following behavior do not explicitly consider a real link between the increase of the speed of movement and the guarantee of the road safety. In this context, this paper presents a decision-making model for determining velocity and safety distance values basing-on a bi-level bi-objective modeling and that allows simulation parameters anticipation. In fact, our model addresses drivers that circulate in urban zones with normative behaviors. The first objective of the model is to allow agent drivers to have a smooth transition between acceleration and deceleration behaviors according to the leading vehicle actions. Simultaneously, the model intends, as a second objective, to reduce the circulation time by increasing the speed of movement. Agent technology and Tabu search algorithm are used respectively to model drivers and to find the best solution during decision-making. The paper provides first a theoretical background of the research. Then it describes the agent driver decision-making model and the resolution algorithm. Finally it presents and discusses a first simulation and experimentations.

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Section: Bi-level Bi-objective Modelingmentioning

confidence: 99%

Section: Resolution Based On the Tabu Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anticipation Based on a Bi-Level Bi-Objective Modeling for the Decision-Making in the Car-Following Behavior

Bennajeh¹,

Kebair²,

Saïd³

et al. 2016

Intelligent Decision Technologies 2016

View full text Add to dashboard Cite

show abstract

“…Though there's also an acceleration, it only affects the driver's comfort with the maximum acceleration being less than 1g (g stands for gravitational acceleration). However, during a crash, the acceleration can reach instantaneously more than ten g, dozens of g, or even hundreds of g [3].…”

Section: Collision Detecting Algorithmmentioning

confidence: 99%

Smart Mobile Phone Groups - Based Design and Implementation of Vehicle Accident Self-rescue System

Zhao

You

Yao

2016

Electron. Sci. Technol. Appl.

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Considering the deficiency that the existing automobile accident self-rescue system requires to assemble the specific sensor, GPS and communication module, the author designed a vehicle collision or crash detection and accident self-rescue system based on the smart mobile phone groups. Taking the acceleration signals of physical moments from the smart mobile phones in the accidents car as the input samples and the assembly of acceleration data as the calculating thresholds, the system can not only improve the accuracy rating of the judgments on the accidents, but can also reduce the unusable distress signals due to the damages to individual vehicle-borne device or mobile phone in severe accidents. When the signals exceed the threshold value, the system can draw support from the functions of video, GPS positioning and 3G networking in the mobile phones automatically to call for help from the rescue center. Meanwhile, a prototype system has been implemented on Android platform. The results of the experiments indicate that the system possesses the advantages of high accuracy of collision or crash recognition, lower energy consumption and cost.

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Driving control based on bilevel optimization and fuzzy logic

Bennajeh¹,

Saïd²

2021

Int J of Intelligent Sys

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Driving control in the car‐following (CF) driving behavior has two aspects. First, in what measure an approximation distance is taken as a safe distance guaranteeing the safety of the follower drivers. Second, how to control the follower's vehicle velocities based on the stimulus of the leading vehicle. In this context, to resolve the driving control problem in the CF driving behavior, a bilevel optimization is presented in this paper, based on the behaviors of the follower and leader drivers. Bearing in mind that mathematics has contributed to the imitation of human behaviors, they are now reaching a level of complexity requiring the entry on the scene of a new player, which is artificial intelligence. Thus, in this paper; we used the fuzzy logic theory for modeling a follower driver with a nonnormative behavior. To validate our model, we used a data set from the program of the US Federal Highway Administration. Therefore, according to the experimental results, there is homogeneity between the actual and the simulated travel trajectories in terms of deviation. Besides, the driver's behavior adopted (normative or nonnormative) is reflected in his reactions to the various components of the road.

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Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars

Cited by 45 publications

References 3 publications

Anticipation Based on a Bi-Level Bi-Objective Modeling for the Decision-Making in the Car-Following Behavior

Anticipation Based on a Bi-Level Bi-Objective Modeling for the Decision-Making in the Car-Following Behavior

Smart Mobile Phone Groups - Based Design and Implementation of Vehicle Accident Self-rescue System

Driving control based on bilevel optimization and fuzzy logic

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