In vehicular ad-hoc networks (VANETs) the impact of vehicles as obstacles has largely been neglected in the past. Recent studies have reported that the vehicles that obstruct the line-of-sight (LOS) path may introduce 10–20 dB additional loss, and as a result reduce the communication range. Most of the traffic mobility models (TMMs) today do not treat other vehicles as obstacles and thus cannot model the impact of LOS obstruction in VANET simulations. In this paper the LOS obstruction caused by other vehicles is studied in a highway scenario. First a car-following model is used to characterize the motion of the vehicles driving in the same direction on a two-lane highway. Vehicles are allowed to change lanes when necessary. The position of each vehicle is updated by using the car-following rules together with the lane-changing rules for the forward motion. Based on the simulated traffic a simple TMM is proposed for VANET simulations, which is capable to identify the vehicles that are in the shadow region of other vehicles. The presented traffic mobility model together with the shadow fading path-loss model can take into account the impact of LOS obstruction on the total received power in the multiple-lane highway scenarios.

Vehicle-to-vehicle (V2V) communication is an emerging technology that has been recognized as a key communication paradigm for safety and infotainment applications in future intelligent transportation systems (ITS). In recent years extensive research efforts have been made to design reliable and fault tolerant vehicular ad-hoc network (VANET) communication protocols. However, the propagation channel is one of the key performance limiting factor which is not yet completely understood [

Several network simulators suitable for VANET simulations exist today, for example, ns-2 [

line-of-sight (LOS)—when the TX vehicle has optical line-of-sight from the RX vehicle;

obstructed-line-of-sight (OLOS)—when the optical LOS between the TX and RX is obstructed by another vehicle.

In the VANET simulators the role of the TMM is very vital in order to perform a realistic system simulations. Today there are a number of traffic models that can be used in the VANET simulators. Some of them are very advanced but equally complex, for example, Simulation of Urban Mobility (SUMO) [

In this paper a TMM is discussed that is capable to identify vehicles being in LOS and OLOS. The TMM is implemented in MATLAB in which the car-following model, which is used to formulate the forward motion of vehicles, is used. The car-following model is of low complexity but gives a realistic traffic flow. The interaction between the lanes is also taken into account by allowing vehicles to perform lane changes when necessary conditioned that the considered vehicles fulfill certain lane change requirements. The model is used to identify the vehicles being in LOS and OLOS from the TX at each time instant. Moreover, the instantaneous position, headway distance, state, distance traveled in each state, and number of transitions from one state to another are logged to calculate the probability of vehicles being in the LOS and OLOS states with respect to distance between them. The traffic simulations are performed based on realistic parameters and the results are compared with the measurement results collected during an independent measurement campaign (for details, see [

The main contribution of this paper is a TMM that is straight-forward to integrate with VANET simulators in order to study the impact of vehicles as obstruction. We do not derive the TMM itself, but we adapt models in the literature to be used for VANET simulations. As mentioned above the TMM is capable to distinguish vehicles that are in LOS and OLOS states on a two-lane highway where the traffic flow is generated by using the lanechanging rules in the car-following model. In addition to that, analytical expressions to find the packet reception probability (PRP) are also provided. The PRP can easily be estimated by utilizing the probability of being in LOS or in OLOS calculated from the TMM into the LOS/OLOS path-loss model proposed in [

The remainder of the paper is organized as follows; the TMM including the car-following model and lane change rules are discussed in Section

In recent years, a number of research efforts have been made to understand and model complex traffic phenomena by using the concepts from statistical physics [

Consider a highway with two lane traffic in each direction of travel and assume that the vehicles in each lane move along a straight line. Let

The car-following traffic model for two-lane traffic.

A microscopic simulation model, the car-following model, is used to describe the movement of vehicles on the same lane. It explains a one-by-one following process of vehicles and incarnate human behaviors which in turn reflects realistic traffic conditions. It has been shown that the car-following model is a better way to model traffic flow compared to the other common traffic-flow models [

The continuous model in (

The above equation can also be written in terms of headways as

The forward difference equations, (

To characterize realistic traffic in a multilane highway scenario it is important to consider interaction between lanes and the lane change activities as it affects stability of the traffic flow. In [

In our simulator each vehicle is allowed to perform lane changes when necessary, conditioned that the vehicle fulfills all lane change requirements. During a lane change event both the lanes are categorized either as the subject-lane or the target-lane. Whenever a vehicle changes lane from the subject to target-lane it becomes a vehicle in the target-lane, and thus the position, number, and identity of each vehicle in both lanes are updated accordingly. It is assumed that the lane change process is instantaneous, so when a vehicle changes lane its longitudinal location remains the same as it was prior to the lane change.

In [

The distance of the vehicle

The distance of the vehicle

Finally the distance

In [

As mentioned before, to date most of the VANET simulators do not consider the impact of line-of-sight obstruction, caused by neighboring vehicles, on the packet reception probabilities. To evaluate this impact in the simulator the TMM is required to identify and label each vehicle as in LOS or in OLOS situation with respect to TX and RX at each instant

Model each vehicle as a screen or a strip with the assumption that each vehicle has the same size.

Assumed that the

Vehicles in each lane are assumed to be moving along a straight line. Thus only two vehicles in the same lane, one at the front and one in the back of the TX, will be in the LOS. The rest of the vehicles in the same lane are considered to be in the OLOS state.

Draw straight lines starting from the antenna position of the TX vehicle touching the edges of the vehicles in the front and back to the edges of road (see Figure

Vehicles that are not bounded by these lines are analyzed individually to see if they are in LOS or in OLOS from the TX.

The identification process is repeated for each vehicle and at each time instant

Identification of vehicles being in LOS and in OLOS of the TX vehicle; vehicles in the shaded-area are considered to be in OLOS whereas all other vehicles have LOS from the TX.

The TMM derived above is implemented in Matlab and simulations are carried out in order to analyze the movement of vehicles over time, their lane changing behavior, and the intensities by which the vehicles change states from LOS-to-OLOS and from OLOS-to-LOS states, respectively. The simulations are performed on a two-lane 14.4 km long circular highway. The circular highway refers to the fact that any vehicle that departs from one end of the highway, that is, beyond 14.4 km, enters from the other end so that the traffic can flow for infinite amount of time. The simulation parameters are chosen as follows.

For the simulations, the initial positions

The initial values of

Let

Practically, the driver’s sensitivity

We let the simulations run for 10800 simulation time steps or seconds that correspond to 3 hours of simulated time. The data obtained from the first 3600 s of simulation is not considered for analysis to ensure that steady-state conditions are obtained. Hence, the time 0 s in the final results corresponds to the time 3600 s of the simulation.

Once the traffic flow is stable the positions and headways of all the vehicles are logged for each time instant, for further analysis, with respect to the vehicles’ identity. The vehicles are allowed to change lane so whenever a vehicle changes lane it exits from subject-lane and becomes part of the target-lane. Thus for every lane change event at each time instant

The headways for three vehicles numbered 60, 120, and 180 are shown as cumulative distribution function (CDF) in Figure

CDFs of the headway distances of vehicles at every second for total simulation time

Further, to record the lane change activities, the total number of lane changes, the position, and time at which lane change occurred were logged over the simulation time for each vehicle. A sample result is shown in Figure

Three vehicles numbered 60, 120, and 180 changing lanes from lane 1 to lane 2 or vice versa between a time window of 35 min to 50 min.

The main focus of this work is to identify the vehicles which are in OLOS from each other so that this information can be used for VANET simulations using the shadow fading path-loss model given in [

A vehicle numbered 20 is assumed to be the TX vehicle which is broadcasting the information with in the intended communication range

CDFs of the total number of vehicles in

Each time a vehicle is in LOS, or in OLOS, it remains in that state for a certain amount of time and travels a distance,

CDFs of (a) the LOS and OLOS intervals for all vehicles and (b) the total distance traveled in the LOS and OLOS by all vehicles.

The number of state transitions,

The CDFs of the state transition intensities ^{−1} and 0.0026 m^{−1}, respectively. For comparison, sample state transition intensities are also calculated from the measurement data collected during a V2V measurement campaign conducted in the city of Lund and Malmö, Sweden, to analyze the shadow fading effects. The measurement data was separated for LOS and OLOS conditions (explained briefly in [^{−1} and ^{−1}, which are close to the mean values of the simulated intensities. The probability of vehicles being in LOS and in OLOS with respect to the distance can also be calculated from the simulation, as shown in Figure

CDFs of the state transition intensities

The probability of LOS and OLOS with respect to distance, and it can be seen that the probability of being in LOS decreases as the distance increases.

In order to evaluate the impact of vehicle as an obstruction on V2V networks the proposed TMM together with the LOS/OLOS path-loss model given in [

To study the performance differences in the PRP with and without considering vehicles as obstacles the LOS/OLOS model is compared with two other aforementioned path-loss models: (1) the LOS only single slope path-loss model by Karedal et al. [

To find an analytical expression for packet reception probability, it is assumed that each vehicle is a point source and vehicles are distributed along a straight line on both lanes of the highway and the probability of LOS and OLOS is known. The parameters of Karedal’s LOS model, Cheng’s Nakagami based model, and LOS/OLOS model are taken from [

Received power as a function of distance. Breakpoint distance of

From the above equations it is obvious that the received power is a Normally distributed with a distance dependent mean

The probability of successful packet reception is shown in Figure

The probability of successful packet reception for a CSTH of −91 dBm.

From Figures

In this paper the effect of line-of-sight (LOS) obstruction is analyzed for vehicle-to-vehicle (V2V) network simulations in a two-lane highway scenario using a traffic mobility model (TMM). A microscopic simulation model, the car-following model, is used to describe the movement of vehicles in the forward direction and the vehicles are allowed to change lane when necessary. Realistic parameters are used for the simulations to achieve a traffic flow being as realistic as possible. Based on the simulated traffic the positions of all vehicles at each instant are recorded. The position information is then used to identify vehicles which are in LOS, obstructed-LOS (OLOS), or out-of-range (OoR) from a selected vehicle that is assumed to be a transmitter in the case of VANET simulations. Vehicles at each instant are defined either in one of the LOS, OLOS, or OoR states. The intensities of vehicles being in each state are logged which can be used to take into account the impact of OLOS in the VANET simulations. The proposed model is straight-forward to implement, gives realistic results, and is based on realistic assumptions for the traffic mobility. Analytical expressions for the packet reception probabilities are used together with the models. The results show the importance of including shadowing by other vehicles for realistic performance assessment.

This work was partially funded by the Excellence Center at Linköping-Lund In Information Technology (ELLIIT) and partially funded by the Higher Education Commission (HEC) of Pakistan.