Automotive Innovation

, Volume 1, Issue 2, pp 158–166 | Cite as

Simulation and Analysis on Overtaking Safety Assistance System Based on Vehicle-to-Vehicle Communication

  • Chunmei Mo
  • Yinong LiEmail author
  • Ling Zheng


Because of the complexity and variability of an intelligent vehicle’s driving environment, it is difficult for the application of the vehicular sensors to meet the needs of the surrounding environment information entirely. Vehicle-to-vehicle (V2V) communication technology is used by target vehicles to exchange information, and obtain the driving condition and driving intention of the front driver. To obtain environmental information outside the range of vehicular sensors in advance, in this paper, a vehicle overtaking assistance system is proposed based on V2V communication. The data, including the speed, position, direction angle and steering angle obtained using V2V communication, were preliminarily processed. Then, combined with an overtaking safety distance model, the vehicle parameters, driver’s driving intention and vehicle status information were entered into an overtaking security assistance system to determine the overtaking conditions. Fuzzy theory was used to control the parameters of the overtaking safety distance model. Finally, the overtaking safety assistance system was established and the proposed algorithm was tested using PreScan/MATLAB cooperative simulation software. The results showed that the proposed overtaking safety algorithm effectively provided a warning according to environmental change and the driver’s intention, which assisted the driver to overtake and avoid the occurrence of accidents, which improved the safety performance of the vehicle.


V2V communication network Overtaking assistance Safe distance Active safety 

1 Introduction

With the rapid development of China’s automobile industry, effectively curbing road traffic accidents and protecting the safety of people’s lives and property is an important development direction of modern automotive technology. Dangerous overtaking is one of the important causes of traffic accidents, and more than 70% of overtaking accidents are caused by human factors, such as lack of driver experience, poor psychological quality, safety distance and front speed judgment error. Simultaneously, external factors, such as an icy pavement, slippery road and poor road shape, also pose a threat to the security of overtaking. Therefore, focusing on an active safety assurance system is an important approach to improve the overall safety performance of vehicles. An overtaking safety assistance system has become a topic of great interest in the field of intelligent transportation.

To improve the active safety of vehicles, researchers have attempted to establish a road overtaking system using a variety of advanced technologies. The vehicle overtaking auxiliary system mainly relies on intelligent vehicle technology, automatic control and artificial intelligence, and uses multi-sensor fusion technology to perceive the surrounding information, analyzes the safe and dangerous states of vehicle overtaking and performs the driver-vehicle-road intelligent information exchange so that the vehicle can drive safely and overtake during unattended operation or with less intervention to reduce the probability of the occurrence of overtaking accidents.

In the first part of the paper, we review the literature on the technological development of overtaking safety system and identify the main challenges presented by overtaking system. In the second part, we present the description of the V2V overtaking model, analyze the overtaking mathematical model and minimum overtaking distance analysis, and include the determination of the overtaking safety distance model parameters using fuzzy theory. Finally, we develop the PreScan/MATLAB simulation experiments for the overtaking safety assistance system based on V2V communication. The experimental results verify the feasibility and effectiveness of the algorithm.

2 Recent Research on Overtaking System

Traditional overtaking assistance systems use camera, radar and lidar [1] technologies. Volvo [2] collects and processes vehicle information in a dead zone using cameras and provides a warning to the driver when overtaking is dangerous. Ruder [3] and the European Union’s prevent project [4] use radar and video integrated systems to monitor the vehicle environment. A warning signal is issued when a hazard occurs.

These solutions are vulnerable to the external environment, detection range, and other factors. For example, bad weather, light intensity, high speed, and other factors exert a great influence on the camera acquisition effect, which directly restricts the camera detection technology in overtaking system applications. Vehicle obstruction results in insufficient data acquisition while limiting the detection range of radar, which affects the reliability of the detection.

However, short-range wireless communication technology can effectively solve the above problems, which has brought new opportunities to overtaking auxiliary system research. Therefore, in recent years, there have been some overtaking auxiliary systems that use wireless communication technology.

Misener [5] used IEEE 802.11b wireless communication technology as the communication medium in an overtaking auxiliary system; however, the real-time performance of the system is not ideal. The European Union’s prevent, United States’ Connected Vehicle Research Program, Japan’s Smart-way and other manufacturers jointly implement the vehicle safety communications applications project, which uses dedicated short-range communications (DSRC) as an overtaking auxiliary system communication technology [6]. Gomes et al. [7] proposed and evaluated the performance of a driver-assistance system that leveraged V2V communication and windshield-installed cameras.

Furthermore, Motro et al. [8] established an overtaking safety system through DSRC-based wireless V2V communication, which was devised to improve safety during overtaking maneuvers on two-lane rural highways. Bae et al. [9] suggested a VANET-based adaptive overtaking assistance system (VAOAS). The VAOAS not only considers both the driving skill of drivers and the length of overtaking/preceding vehicles but also supports all strategies of overtaking using VANETs.

Regarding the estimation of overtaking risk, Toledo-Moreo et al. [10] predicted when a potential lane change was going to be performed by the driver of the ego-vehicle and cooperatively exchanged information with vehicles traveling in the opposite direction via the cellular network.

In the intelligent overtaking decision-making algorithm, Khan [11] provided algorithm and hardware implementation details for an advanced overtake assist feature. Additionally, Li et al. [12] used reinforcement learning algorithms to learn an optimized policy via a series of simulated scenarios for highway autonomous driving.
Fig. 1

Overtaking process

The research on overtaking systems has made some achievements; however, the overtaking safety auxiliary system has not been popularized, mainly because the overtaking process is very complicated and influenced by many unstable factors. At present, there is little research on an overtaking safety model combined with information that includes accurate parameters of the vehicle, driving intention of the front driver, driver’s real-time operation information and vehicle status information obtained by V2V communication technology.

In this paper, overtaking safety assistance system is established using vehicle overtaking safety distance model and multi-vehicle information interaction based on wireless communication technology.

3 Overtaking Safety Assistance System

3.1 Description of the V2V Overtaking Model

The V2V overtaking safety assistance system is a V2V application for which alerts are provided to drivers to help them to avoid a head-on crash that results from passing maneuvers. Using the DSRC receiver (Rx) and transmitter (Tx), data between actors can be exchanged using a selected DSRC protocol and selected message types. The formation of a temporary wireless network exchanges information with any of the vehicles in the network using the relevant wireless communication equipment installed in the intelligent vehicles. Basic safety message (BSM) part 1 of DSRC transmitter inputs includes the following signals which all have a name in the Simulink representation. Part 2 of BSM has a lot of optional signals. The following signals can be used in PreScan: Eventflag, crumbData, itemCnt, radiusOfCurve, Confidence, ExteriorLights, ThrottlePosition. Since the signals of BSM are optional, they can be selected and deselected, and only a part of the required signals are sent to the encoder.

When a driver is overtaking, the driving intention of the front vehicle and the driving condition of the opposite lane are important factors to be considered. If the driver can be informed of the other driver’s driving intention and the vehicle state of the opposite lane in advance before performing the overtaking behavior, then the system can make a relatively accurate judgment based on different circumstances. Thus, the drivers should make adjustments to their driving behavior, such as abandoning overtaking or slowing down, in a timely manner to avoid traffic accidents. Figure 1 shows an overtaking process.

In view of this scenario, in this paper, we consider two-way and two-lane roads as the background, and information communication between vehicles is achieved by virtue of a V2V communication module. Before the implementation of overtaking behavior, the driver can be informed in advance of the front vehicle’s driving intention in addition to the traffic condition of the opposite lane. An algorithm based on the overtaking safety distance model is proposed to prevent the confusion of drivers regarding deciding whether safe overtaking is possible and avoiding a collision with oncoming vehicles while overtaking.

The overtaking safety algorithm flow is as follows: (a) Collect the travel status data of vehicles using the data acquisition modules installed in the vehicle. (b) Establish the vehicle network using V2V communication. The collected travel data are sent to other vehicles, and the driving state data from other vehicles are received. (c) The relative position of the host vehicle and target vehicles, and the overtaking alarm distance are obtained by combining the overtaking safety distance model algorithm [13] and the V2V information. If there is a potential collision risk, then a reminder signal is sent to the driver. If the driver had started the overtaking behavior, then a signal is sent to the driver so that it can take safety measures.

3.2 Overtaking Safety Distance Model

On two-way and two-lane roads, vehicles must occupy the opposite lane in the process of overtaking. The vehicle can only meet the conditions of a sufficient overtaking safety distance if it can change the lane to overtake; otherwise, it can only slow down to keep driving with front vehicle B. A safe safety distance refers to the shortest distance for the host vehicle to travel from the beginning of overtaking to the end of overtaking. The entire overtaking process must ensure that there is no possibility of collision with front vehicle B and vehicle C in the opposite lane. From the perspective of modeling, the overtaking model can be divided into uniform speed overtaking and accelerated overtaking.
Fig. 2

a Uniform speed overtaking model b Accelerated overtaking model

3.2.1 Uniform Speed Overtaking Model

The travel speed of host vehicle A, front vehicle B and oncoming vehicle C is constant. During overtaking, the time that elapses from the A1 to A4 position of vehicle A is t. After time t is experienced, vehicle C travels from the C1 to C2 position, and vehicle B travels from the B1 to B2 position, as shown in Fig. 2a.

\(S_1 \) and \(S_2 \) are the minimum safe distances that must be guaranteed between host vehicle A and vehicle B, respectively, when vehicle A starts to change lane and immediately after it returns to the original lane to complete overtaking, where \(S_1 \ge 20\,\hbox {m}\). \(S_3 \) is the minimum safe distance that must be guaranteed between host vehicle A and vehicle C when turns to the original lane to complete overtaking. \(S_1 \), \(S_2 \) and \(S_3 \) should be measured in advance; however, they vary with the environmental conditions and vehicle speed. The speeds of vehicles A, B and C are \(v_\mathrm{A}\), \(v_\mathrm{B}\) and \(v_\mathrm{C}\), respectively. The overtaking safety distance of the uniform speed overtaking model is
$$\begin{aligned} S_{\mathrm{uniform}\_\mathrm{speed}} =(S_1 +S_2 )(v_\mathrm{A} \hbox {+}v_\mathrm{C})/(v_\mathrm{A} - v_\mathrm{B})+S_3 \end{aligned}$$

3.2.2 Accelerated Overtaking Model

In the process of accelerated overtaking, vehicle A first accelerates at \(t_\mathrm{{a}}\) and then maintains a uniform speed at \(t_c \), whereas vehicle B, C travels with uniform speed the entire time. During \(t_\mathrm{a} \), vehicle A travels from the A1 to A3 position. During \(t_\mathrm{c} \), vehicle A travels from the A3 to A4 position. \(S_4 \) is the distance between vehicles A and B when vehicle A is in the A3 position, as shown in Fig. 2b. The value of \(S_4 \) can be negative or positive. \(S_1 \), \(S_2 \) and \(S_3 \) are the same as in the uniform speed overtaking model:
$$\begin{aligned} \left\{ \begin{array}{l} t_\mathrm{a} =((v_\mathrm{B} - v_\mathrm{A} )+\sqrt{(v_\mathrm{B} - v_\mathrm{A} )^{2}+2a(S_1 +S_4 )})/a\\ t_\mathrm{c} =(S_2 -S_4 )/\sqrt{(v_\mathrm{B} -v_\mathrm{A} )^{2}+2a(S_1 +S_4 )}\\ S_\mathrm{accelerate} = (v_\mathrm{A} \hbox {+}v_\mathrm{c} )(t_\mathrm{a} +t_\mathrm{c} )+{at_a^2 }/2+at_a t_c +S_3 \end{array} \right. \end{aligned}$$

3.3 Parameter Determination Based on Fuzzy Theory

A vehicle’s driving data can be obtained using v2v communication technology, such as speed, acceleration, location and other information, whereas other parameters in the model require predetermination. Because the vehicle overtaking process is extremely complex, the vehicle’s driving states are affected by factors that include weather conditions, road conditions, and the driver’s physical and psychological conditions. Therefore, based on the fuzzy theory, the fuzzy relation between the various factors is determined in the process of determining the model parameters. Additionally, fuzzy reasoning is used to obtain the parameters that are much closer to the real world.
Fig. 3

FIS output surface observation window of \(S_2 \)

\(t_1 \) is the sum of the reaction time and action time. In this paper, \(\tau _1 = 1.2\) s and \(\tau _2 = 0.3\) s. \(t_2 \) is the acceleration linear growth time: \(t_2 = 0.2\) s. The vehicle overtaking acceleration is \(a = 0.69\,\mathrm{m}\,\mathrm{s}^{-2}\) . When the vehicle stops, the two vehicles should also maintain a certain safety distance: \(\Delta S=3\) m. The adhesion coefficient of the pavement is a key factor that affects the braking distance, and different pavement types and pavement conditions have different attaching factors. The four fuzzy subsets of the defined input pavement are {Ice, Soil, Wet, Dry}. The domain of the pavement is [0, 1]. The membership function of the pavement is shown in Eq. 3. the five fuzzy subsets of the defined input velocity are {low, mid, mid to high, high, very high}. The domain of the velocity is [0, 160]. the membership functions of the pavement and velocity, respectively, are as follows:
$$\begin{aligned} \left\{ \begin{array}{ll} \begin{array}{ll} {\mathrm{Ice\,(Pav)}=\dfrac{0.32-\mathrm{Pav}}{0.31}}&{} {0.1\le } \\ \end{array} \mathrm{Pav}\le 0.32\\ \mathrm{Soil\,(Pav)}=\left\{ \begin{array}{l} \begin{array}{ll} {\dfrac{\mathrm{Pav}}{0.31}}&{} {0.1\le \mathrm{Pav}\le 0.32} \\ \end{array} \\ \begin{array}{ll} {\dfrac{0.65-\mathrm{Pav}}{0.33}}&{} {0.32\le \mathrm{Pav}\le 0.65} \\ \end{array} \\ \end{array} \right. \\ \mathrm{Wet \,(Pav)}=\left\{ \begin{array}{l} \begin{array}{ll} {\dfrac{\mathrm{Pav}-0.32}{0.33}}&{} {0.32\le \mathrm{Pav}\le 0.65} \\ \end{array} \\ \begin{array}{ll} {\dfrac{1-\mathrm{Pav}}{0.35}}&{} {0.65\le \mathrm{Pav}\le 1} \\ \end{array} \\ \end{array} \right. \\ \begin{array}{ll} {\mathrm{Dry\, (Pav)}=\dfrac{\mathrm{Pav}-0.65}{0.35}}&{} {0.65\le } \mathrm{Pav}\le 1 \end{array} \end{array} \right. \end{aligned}$$
$$\begin{aligned}&\left\{ {\begin{array}{ll} {\begin{array}{ll} {\mathrm{Low}\,(v_\mathrm{A} )=\dfrac{50-v_\mathrm{A} }{50}}&{} {0\le } \\ \end{array} }v_\mathrm{A} \le 50 \\ \mathrm{Mid}\,(v_\mathrm{A} )=\left\{ {{\begin{array}{l} {{\begin{array}{ll} {\dfrac{v_\mathrm{A} }{50}}&{} {0\le v_\mathrm{A} \le 50} \\ \end{array} }} \\ {{\begin{array}{ll} {\dfrac{90-v_\mathrm{A} }{40}}&{} {50\le v_\mathrm{A} \le 90} \\ \end{array} }} \\ \end{array} }} \right. \\ \mathrm{Midtohigh}\,(v_\mathrm{A} )=\left\{ {{\begin{array}{l} {{\begin{array}{ll} {\dfrac{v_\mathrm{A} -50}{40}}&{} {50\le v_\mathrm{A} \le 90} \\ \end{array} }} \\ {{\begin{array}{ll} {\dfrac{120-v_\mathrm{A} }{30}}&{} {90\le v_\mathrm{A} \le 120} \\ \end{array} }} \\ \end{array} }} \right. \\ \mathrm{High}\,(v_\mathrm{A} )=\left\{ {{\begin{array}{ll} {{\begin{array}{ll} {\dfrac{v_\mathrm{A} -90}{30}}&{} {90\le v_\mathrm{A} \le 120} \\ \end{array} }} \\ {{\begin{array}{ll} {\dfrac{160-v_\mathrm{A} }{40}}&{} {120\le v_\mathrm{A} \le 160} \\ \end{array} }} \\ \end{array} }} \right. \\ {\begin{array}{ll} {\mathrm{Veryhigh}\,(v_\mathrm{A} )=\dfrac{v_\mathrm{A} -120}{40}}&{} {120\le } \\ \end{array} }v_\mathrm{A} \le 160 \\ \end{array}} \right. \end{aligned}$$
The deceleration caused by vehicle braking varies with the vehicle type, load condition and road condition. The pavement conditions are the cardinal factors that affect the maximum deceleration. The fuzzy relationship between the maximum deceleration \(a_{\max } \), pavement condition and vehicle velocity is established using fuzzy theory to comprehensively evaluate the maximum deceleration in the current driving environment. The domain of \(a_{\max } \)is [0, 10]. The specific fuzzy reasoning process is not described here. \(S_2 \) is mainly affected by the speed of vehicle B and the pavement conditions. According to the principle of fuzzy theory, the pavement and speed are set to input and \(S_2 \) is set to output. The five fuzzy subsets of \(S_2 \) are {very near, near, middle, remote, far}. The domain of \(S_2 \) is [20, 40]. The fuzzy logic algorithm of \(S_2 \) was designed using the fuzzy toolbox. The FIS output surface observation of \(S_2 \) is shown in Fig. 3. When the vehicle is overtaking at a uniform speed, \(t_\mathrm{a} =0\). \(S_3 \) is as shown in Eq. (5). Based on the past experiences, when vehicle A stops accelerating, its speed is at least greater than the speed of vehicle B, and the speed difference is \(\Delta v = 20\,km/h\). \(S_4 \) is as shown in Eq. (8). \(S_2 \) at different velocity and road conditions are shown in Table 1.
$$\begin{aligned}&S_3 ={v}'_\mathrm{A} {t}_{1} +({v}'_\mathrm{A} -v_\mathrm{C})t_{2} /2+{v'}_\mathrm{A}^{2}/2a_{\max }\nonumber \\&\qquad \quad + v_\mathrm{C}^{2}/2a_{\max } +\Delta S \end{aligned}$$
$$\begin{aligned}&{{v}'_\mathrm{A}} ={v_\mathrm{A}} + {at_\mathrm{a}} \end{aligned}$$
$$\begin{aligned}&{v_\mathrm{A}} + {at_\mathrm{a}} = {v_\mathrm{B}} +\Delta v \end{aligned}$$
$$\begin{aligned}&S_4 ={((\Delta v)^{2}-(v_\mathrm{B} -v_\mathrm{A} )^{2}-2aS_1 )}/{2a} \end{aligned}$$
Table 1

\(S_2 \) at different velocity and road conditions







Low (0–50)





Mid (50–90)





Mid to High (90–120)





High (120–160)





Very high (>160)





4 Simulation Testing

The proposed overtaking safety assistance system was implemented and tested in the PreScan environment [14]. In the early stage of the development of the intelligent vehicle system, the algorithm was tested and verified using simulation software, which reduced the development cost and provided an important reference for the improvement of the algorithm. PreScan is a simulation platform that consists of a graphical user interface (GUI)-based preprocessor to define scenarios and a run-time environment to execute them. The engineer’s prime interface for developing and testing algorithms includes MATLAB/Simulink. PreScan comprises several modules that together provide radar, camera and other sensors for ADAS system developers. An intuitive GUI allows the scenario to be built and the sensors to be modeled, whereas the MATLAB/Simulink interface enables a control system to be added. This interface can also be used to import existing MATLAB/Simulink models, such as vehicle dynamics models. When running the experiment, the visualization viewer provides a realistic three-dimensional representation of the scenario.

In the following subsections, we describe the development and testing of the auxiliary overtaking safety system simulation using PreScan. In this chapter, the PreScan was adopted to establish a virtual traffic scene. Carsim software was applied to build a car dynamics model, and the co-simulation simulation platform was established with MATLAB/Simulink. The effectiveness of the algorithm based on V2V overtaking safety strategy designed in this paper was verified by simulation.

4.1 Build the Experimental Scenario

This experiment was easily built in PreScan’s GUI using drag and drop actions for the library elements of road sections, infrastructure components (buildings and traffic signs), actors (vehicles, bikes and pedestrians), sensors, weather conditions (rain and snow) and light sources (headlights and sun). A simulation scenario is shown in Fig. 4. This scenario illustrates an overtaking safety assistance system application based on V2V communication via DSRC. The host vehicle is equipped with a DSRC receiver, whereas all target vehicles are equipped with DSRC transmitters. All vehicles drive in accordance with predefined trajectories and predefined speed profiles.
Fig. 4

Simulation scenario

In PreScan, rain was defined as a certain number of semi-transparent particles per volume, with a certain diameter, and falling at a certain velocity. Rain is visible on cameras. Furthermore, the chosen rain category influences radar only via the implicit definition of the attenuation. For other sensors, the influence of rain should be modeled via the attenuation.

4.2 Add the Overtaking Safety Warning Control Algorithm

The MATLAB/Simulink interface enables users to design and verify algorithms for data processing, sensor fusion, decision making and control in addition to the use of existing Simulink models, such as vehicle dynamics models from CarSim, Dyna4 or ASM.

As shown in Fig. 5, the vehicle dynamics model established by Carsim software and the virtual environment created by PreScan are imported into Simulink to build a joint simulation platform. The joint simulation platform includes data acquisition model, V2V network, overtaking safety decision model, vehicle Carsim model, parameters design model and virtual environment model. The vehicle data acquisition model exchanges information via DSRC receiver and transmitter.
Fig. 5

Joint simulation platform

Establishing an accurate vehicle dynamics model is the basis for vehicle control. Car dynamics modeling software CarSim was used to establish vehicle dynamics model. The software can accurately reflect the vehicle’s response to driver input, ground input, aerodynamic influences, and output evaluation parameters such as stability, dynamics, and ride comfort. The input and output interfaces are freely set by the user and have good operability and scalability. The vehicle dynamics model established in this paper adopts B-Class and Hatchback models. The specific parameters are shown in Table 2.

4.3 Simulation Result

The vehicle’s latitude and longitude, speed, direction and other travel information were received in real-time using the GPS positioning module. Figure 9 shows the lateral position of vehicles, the longitudinal position of vehicles and the range between the host vehicle and target vehicle.

Case A In the entire uniform overtaking process, all vehicles run at a uniform speed on a sunny day. The speeds of host vehicle A, front vehicle B and oncoming vehicle C are 22.22, 16.7 and 22.22 m/s, respectively. In case A, \(S_1 = 25.2\,\hbox {m}\), \(S_2 = 24.8\,\hbox {m}\), \(S_3 = 98.81\,\hbox {m}\) and \(S_\mathrm{uniform~speed} = 503.61\,\hbox {m}\) which were calculated using the overtaking safety model algorithm. At this time \(S_\mathrm{host-vehicle} =~136.64\,\hbox {m}\) and \(S_\mathrm{host-vehicle} < S_\mathrm{uniform~ speed}\) which means that the safety distance requirements were not met. Vehicle A obtained a reminder when \(S_{\mathrm{host-vehicleC}}=200\,\hbox {m}\), and obtained a warning when the vehicle started to overtake, as shown in the lower scope of Fig. 6. Reminders are shown in yellow, and warnings are shown in red. A reminder is less intrusive than a warning. For example, a reminder can be a light on the dashboard panel, whereas a warning can be a beeping sound. The various threshold values of reminders can be adapted by the user.
Table 2

Vehicle dynamics parameters

Vehicle dynamic parameters



Vehicle quality



Rotational inertia around Z axis



Center of mass to front axle distance



Center of mass to rear axle distance



Centroid height



Body width



Case B Vehicle A performs accelerated overtaking on a sunny day. Before host vehicle A starts accelerated overtaking, the speeds of host vehicle A, front vehicle B and oncoming vehicle C are 16.67, 13.89 and 11.11 m/s, respectively. In case B, \(S_1 =25.71\,\hbox {m}\), \(S_2 =22.8\,\hbox {m}\), \(t_\mathrm{a}=4.014\,\hbox {s}\), \(t_\mathrm{c} =5.726\,\hbox {s}\), \(S_3 =56.88\,\hbox {m}\), \(S_4 = -8.98\,\hbox {m}\) and \(S_\mathrm{accelerate}=294.69\,\hbox {m}\), which were calculated using the overtaking safety model algorithm. At this time \(S_\mathrm{host-vehicle}=304.23\,\hbox {m}\), and \(S_\mathrm{host-vehicle} < S_\mathrm{uniform~ speed}\), which means that the safety distance requirements were met. The process of vehicle accelerated overtaking on a sunny day is shown in Fig. 7.

Case C In addition to changing the weather from sunny to rainy, the details are the same as Case B. In case C, \(S_1 =25.71\,\hbox {m}\), \(S_2 =28.8\,\hbox {m}\), \(t_\mathrm{a} =4.014\) s , \(t_\mathrm{c}=6.821\,\hbox {s}\), \(S_3 =64.29\,\hbox {m}\), \(S_4 = -8.98\,\hbox {m}\), \(S_\mathrm{accelerate} =389.05\,\hbox {m}\) and \(S_\mathrm{host-vehicle} < S_\mathrm{uniform~speed}\). Vehicle A obtained a warning, as shown in Fig. 8. Reminders are shown in yellow and warnings are shown in purple.
Fig. 6

Vehicle obtains a warning when uniform overtaking on a sunny day

Fig. 7

Process of vehicle accelerated overtaking on a sunny

Fig. 8

Vehicle obtains a warning when accelerated overtaking on a rainy day

Fig. 9

GPS position of the vehicle and range

5 Conclusions

In this paper, an overtaking system based on vehicle communication was proposed. The parameters of the safety distance model were determined based on fuzzy theory control. V2V technology was used to implement wireless transmission and reception of information between vehicles. The real-time communication between vehicles in the range of communication was achieved before the implementation of overtaking behavior, which made the identification of the opposite lane traffic and the driving intention of front vehicles accurate. The results showed that the proposed algorithm was feasible and effective, which reduced the risk of overtaking. Simultaneously, the research in this paper also accelerates the use of V2V technology in the field of intelligent vehicle and improves intelligent vehicle environment perception ability. The main conclusions include following points:
  1. 1.

    From the point of view of the overtaking starting speed difference, the two overtaking schemes of uniform overtaking and accelerating overtaking were studied, and the mathematical model of the overtaking safety distance was established.

  2. 2.

    Taking into account the weather conditions, road conditions and other external environmental factors, and the impact of vehicle speed, the parameters involved in the model and the fuzzy relationship between the partial parameters and various influencing factors were determined using fuzzy theory. Additionally, the fuzzy inference rules of partial parameters were established using a fuzzy relation.

  3. 3.

    The PreScan/MATLAB cooperative simulation model of the vehicle overtaking anti-collision early warning system based on V2V communication was established, and experiments under different weather conditions were conducted. The rationality of the safety distance model and parameter setting was verified according to the test results.




The authors acknowledge the National Key Research and Development Program of China under Grants (2016YFB0 100904, 2017YFB0102603) and Chongqing Science and Technology Commission under Grants (cstc2015jcyjBX0097, csts2015zdcy-ztzx30001) for financial support.


  1. 1.
    Vasic, M., Lederrey, G., Navarro, I., et al.: An overtaking decision algorithm for networked intelligent vehicles based on cooperative perception. In: 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, pp. 1054–1059 (2016)Google Scholar
  2. 2.
    Wang, X.M.: Auxiliary system for active safety technology of automobile. Electromech. Technol. 02(01), 150–152 (2013)Google Scholar
  3. 3.
    Visvikis, C., Smith, T.L., Pitcher, M.: Study on lane departure warning and lane change assistant systems. Transport Research Laboratory Project Rpt PPR, no. 01, p. 374 (2008)Google Scholar
  4. 4.
    Lee, Y.D., Li, Z.Z., Zhang, S.R.: Safety impacts of red light running photo enforcement at urban signalized intersections. J. Traffic Transp. Eng. Engl. Edit. 01(05), 309–324 (2014)Google Scholar
  5. 5.
    Tideman, M.: A new seenario based approach for designing driver support system applied to the design of a lane change support system. Transp. Res. Part C Emerg. Technol. 18(02), 247–258 (2010)CrossRefGoogle Scholar
  6. 6.
    Ju, H.Q.: Research and application of short distance communication technology in taxi pricing management system. Microcomput. Appl. 21, 56–59 (2011)Google Scholar
  7. 7.
    Gomes, P., Olaverri-Monreal, C., Ferreira, M.: Making vehicles transparent through V2V video streaming. IEEE Trans. Intell. Transp. Syst. 13(2), 930–938 (2012)CrossRefGoogle Scholar
  8. 8.
    Motro, M., Chu, A., Choi, J., et al.: Vehicular ad-hoc network simulations of overtaking maneuvers on two-lane rural highways. Transp. Res. Part C Emerg. Technol. 72, 60–76 (2016)CrossRefGoogle Scholar
  9. 9.
    Bae, I.H., Lee, J.K.: Design and performance evaluation of a VANET-based adaptive overtaking assistance system. Lect. Notes Electr. Eng. Adv. Multimed. Ubiquitous Eng. Future Inf. Technol. 354, 59–69 (2016)CrossRefGoogle Scholar
  10. 10.
    Toledo-Moreo, R., Santa, J., Zamora-Izquierdo, M.A.: A cooperative overtaking assistance system. In: Iros 2009 3rd Workshop: Planning Perception and Navigation for Intelligent Vehicles (2009)Google Scholar
  11. 11.
    Khan, J.: Using ADAS sensors in implementation of novel automotive features for increased safety and guidance. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, pp. 753–758 (2016)Google Scholar
  12. 12.
    Li, X., Xu, X., Zuo, L.: Reinforcement learning based overtaking decision-making for highway autonomous driving. In: 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP), Wuhan, pp. 336–342 (2015)Google Scholar
  13. 13.
    Xu, Y., Yue, D.J., Zhao, Y.: Study on safety auxiliary judgment system of vehicle overtaking. Agric. Equip. Veh. Eng. 05(03), 19–22 (2010)Google Scholar
  14. 14.
    Tideman, M., Noort, M.: A simulation tool suite for developing connected vehicle systems. In: 2013 IEEE Intelligent Vehicles Symposium (IV), 23–26 June 2013Google Scholar

Copyright information

© Society of Automotive Engineers of China (SAE-China) 2018

Authors and Affiliations

  1. 1.College of Automotive EngineeringChongqing UniversityChongqingChina

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