Extended Desired Safety Margin Car-Following Model That Considers Variation of Historical Perceived Risk and Acceptable Risk

2018 ◽  
Vol 2672 (20) ◽  
pp. 86-97 ◽  
Author(s):  
Junjie Zhang ◽  
Yunpeng Wang ◽  
Guangquan Lu
Keyword(s):  
Traffic Safety ◽  
Perceived Risk ◽  
Dynamic Performance ◽  
External Disturbance ◽  
Safety Margin ◽  
Cruise Control ◽  
Acceptable Risk ◽  
Car Following ◽  
Gray Correlation ◽  
Car Following Model

In this paper, an extended desired safety margin (DSM) car-following model is proposed by accounting for the effect of the variation of historical perceived risk and acceptable risk in terms of the DSM model. By conducting gray correlation analysis, an investigation is carried out into whether the variation of historical perceived risk and acceptable risk has important effects on the acceleration or deceleration of a target vehicle based on a real-vehicle test platform. Through simulated results, the dynamic performance of an extended DSM model is investigated in comparison with the DSM model. Conclusions show that the extended DSM model can better reflect the characteristics of traffic flow compared with the DSM model, and can improve the performance of the vehicle in the start, stop, and car-following processes. Numerical simulations further demonstrate that the extended DSM model can improve traffic safety via the changes of time-to-collision and safety margin when external disturbance is introduced into the leading vehicle. Thus, historical information about target vehicles should be considered to improve the performance of car-following in adaptive cruise control (ACC) and automotive platoon driving.

scholarly journals A Car-Following Model Based on Quantified Homeostatic Risk Perception

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Guangquan Lu ◽  
Bo Cheng ◽  
Yunpeng Wang ◽  
Qingfeng Lin
Keyword(s):  
Perceived Risk ◽  
Safety Margin ◽  
Model Parameters ◽  
Perceived Safety ◽  
Car Following ◽  
Stimulus Response ◽  
Proposed Model ◽  
Car Following Model ◽  
The Difference ◽  
Level Of Risk

This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT.

Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu
Keyword(s):  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersection ◽  
Environmental Benefit ◽  
Cruise Control ◽  
Car Following ◽  
Vehicle Type ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model ◽  
Fuel Consumption And Emissions

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driver Model ◽  
Cruise Control ◽  
Maximum Acceleration ◽  
Car Following ◽  
Time Headway ◽  
Asphalt Road ◽  
Car Following Model ◽  
Maximum Deceleration

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.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

Linear Acceleration Car-Following Model Development and Validation

1998 ◽  
Vol 1644 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M.F. Aycin ◽  
R.F. Benekohal
Keyword(s):  
Intelligent Transportation Systems ◽  
Field Data ◽  
Statistical Tests ◽  
Reaction Times ◽  
Linear Acceleration ◽  
Transportation Systems ◽  
Cruise Control ◽  
Vehicle Simulation ◽  
Car Following ◽  
Car Following Model

A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.

scholarly journals Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

2022 ◽  
Vol 2 (1) ◽  
pp. 24-40
Author(s):  
Amirhosein Karbasi ◽  
Steve O’Hern
Keyword(s):  
Case Studies ◽  
Human Error ◽  
Road Traffic ◽  
Driver Model ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Road Traffic Crashes ◽  
Car Following ◽  
Car Following Model ◽  
The Impact

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

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