Simulation and Testing of a Platooning Management Protocol Implementation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10372)


VANETs (Vehicular Ad Hoc Networks) are networks of moving vehicles equipped with devices that allow spontaneous communication. Developing collaborative applications for VANETs has currently an increasing popularity in the Intelligent Transportation Systems (ITS) domain. This paper proposes a Platooning Management Protocol (PMP), whose implementation and testing is carried out by means of simulation, using the V2X Simulation Runtime Infrastructure (VSimRTI) framework (coupling Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3)). Results show that PMP works in a efficient manner: maneuvers happen during an acceptable time interval, the proposed communication requirements are met and the lane capacity is increased.


Platooning ITS Simulation VANETs 

1 Introduction

ITS consist of an intricate set of technologies applied to vehicles and infrastructures that ensure an efficient and smart usage of the roads in general, which potentially improve safety, efficiency and productivity or even decrease levels of pollution. ITS enable the rise of several applications relying on the exchange of information between vehicles themselves and infrastructures, allowing drivers to make smarter driving choices. The goal of this work is to develop and test a PMP that defines several maneuvers to allow platooning (create, join, leave, merge and dissolve), including the set of messages that allow their operation. The structure of this paper is as follows: first, the state of the art regarding ITS application development is presented. Next, the PMP is introduced, analyzed and tested. The simulation environment is also discussed, along with the results obtained from the simulations.

2 Related Work

This section provides a brief overview on available publications that cover subjects related to V2X applications, specially advanced applications. The work in [1] presents a Cooperative Adaptive Cruise Control (CACC) system that aims to reduce significantly the gaps between the vehicles, taking advantage from information exchanged using Dedicated Short-Range Communications (DSRC) wireless communication. In [2] is presented a CACC implementation at the Grand Cooperative Driving Challenge (GCDC), based on Vehicle to Vehicle (V2V) communication. In [3], the interference of non-automated vehicles, when a given vehicle is joining a platoon is analyzed. It is defined a protocol that supports the join maneuver and it is validated using PLEXE from Vehicles in Network Simulation (VEINS). A CACC management protocol based on IEEE 802.11p communication, including three basic maneuvers (merge, split and lane change) is presented in [4]. In [5], communication strategies for Platooning are investigated and compared to typical beaconing protocols, resorting to PLEXE. In [6], an application that aims to advise danger on emergency situations on VANETs resorting to IEEE 802.11p is proposed. Additionally, there are some important projects focused on the study of advanced ITS applications, such as COMPANION, iGAME or SARTRE.
Fig. 1.

Platoon of trucks

3 Platooning Management Protocol

Platooning is a solution that allows vehicles to travel very close to each other in groups with automated velocity and steering control. Driving in platoons with automatic control enables the enhancement of safety, traffic flow and highway capacities, while providing drivers with a more convenient and comfortable driving experience. Furthermore, it helps to save energy and fuel, while reducing emissions [7, 8]. Figure 1 illustrates a platoon of trucks in an highway. The simplest way of implementing Platooning is through the use of V2V communication, where vehicles only share information with their immediate predecessor. More advanced solutions disseminate information from vehicles that are not in line of sight, providing the driver with situational awareness feedback. Vehicles possessing group information in advance helps to predict the behavior of the platoon. Platooning requires a very efficient PMP that specifies all the required maneuvers and proper communication behaviors. The proposed PMP is described next, including a description of the maneuvers and the specification of their requirements based on European standards.

3.1 Maneuvers

The Create maneuver starts when a given vehicles tries to join a platoon but there are no available strings around him. The process of creating a platoon is: (i) Leader vehicle starts a new Platoon; (ii) Leader vehicle propagates the Platoon existence, broadcasting its ID every second.

The Join maneuver is triggered when a vehicle wants to join a platoon. An important aspect of the Join maneuver is the string ordering. The simplest solution is to make vehicles join the platoon tail. Allowing vehicles to join in any position, enables the string to be ordered by several parameters: e.g. braking performance. In these cases, vehicles open a gap that allows the joining vehicle to merge, which requires more coordination. A vehicle is able to join a platoon if the string does not exceed its maximum length and if no other maneuver is occurring. The joining process is: (i) Joiner sends a periodical Join Request broadcast; (ii) Leader responds with a Join Acknowledgment if it’s possible to join. Otherwise, it responds with a Join Reject; (iii) Joiner moves to the correct position to change lane and informs the Leader with a Distance Achieved message; (iv) Leader notifies the Followers to open up a gap, with a Adjust Gap message (unless the joining is by the rear); (v) Followers notify the Leader when the adjusting process is completed with Adjust Gap Acknowledgments; (vi) Leader sends a Start Maneuver message, informing the Joiner that the maneuver can be accomplished; (vii) Joiner changes lane and enters automatic mode, notifying the Leader with a Maneuver Completed message. viii) Leader sends a Platoon Update message for all Followers with updated information.

The Leave maneuver is initiated when a Follower needs to exit the platoon. It informs the Leader and waits for its response, before assuming manual control. Only one vehicle may leave the platoon at a time and only if the other followers have confirmed to adjust their gap. The maneuver steps are: (i) Follower sends a Leave Request; (ii) Leader orders Followers to open a gap with Adjust Gap messages; (iii) Followers acknowledge their adjustments, resorting to Adjust Gap Acknowledgment messages; (iv) Leader returns a Start Maneuver message for the Leaver; (v) Leaver shifts to manual driving and changes lane; (vi) Leaver notifies the Leader with a Maneuver Completed message; (vii) Leader notifies Followers that the maneuver is finished with Platoon Update messages.

The Dissolve maneuver happens when the Leader decides to disassemble the string. The Leader may only dissolve after all Followers acknowledge the command. The steps are: (i) Leader sends a Dissolve Request; (ii) Followers enter manual driving mode and send a Dissolve Acknowledgment to the Leader; (iii) When all Followers respond (if any), the Leader dissolves the platoon.

The Merge maneuver consists on joining two platoons. This maneuver is only possible if the size of the platoons is less than the maximum length and the process is initiated by the Rear Leader. The following steps show how the Merge maneuver is performed. The front Leader and platoon are referred as Leader A and Platoon A, while the rear Leader and platoon are referred as Leader B and Platoon B: (i) Leaders send Merge Requests every 10 s; (ii) Leader A receives the request and responds with a Merge Acknowledgment; (iii) Leaders exchange Platoon Info messages with information regarding their platoons; (iv) Leader A sends a Adjust Gap message to Leader B; (v) Leader B moves Platoon B to the rear of Platoon A; (vi) Leader B acknowledges the distance with a Adjust Gap Acknowledgment message; (vii) Leader B sends a New Leader message to its Followers; (viii) Leader B assumes a Follower role.

3.2 Platooning Requirements

The most important requirements for the platooning application are based on the ETSI TR 102 638 standard [9], which provides the main requirements for a Co-operative vehicle-highway automation system (Platoon) use case. The latency is defined to have a maximum value of 100 ms, the relative position accuracy should be better than 2 m, and the platoon group messages should have a minimum frequency of 2 Hz. The vehicles should be prepared to transmit V2V messages in unicast and broadcast mode.

4 Simulation Deployment

The development of efficient VANETs systems requires the determination of its main properties and consequent evaluation of its performance. Performing field tests is a tough challenge: the large number of existent vehicles and scenarios makes it harder to collect data, the development of prototypes is expensive, etc. Simulation is a popular solution to evaluate the performance of ITS systems - tests are easily repeated and researchers are able to control parameters, configurations, conditions and input data. However, it normally assumes the use of simpler models, which may reduce the system realism. To perform a proper simulation of VANETs, both a traffic and a network simulator are required. Network simulation is one of the most prominent evaluation methods in computer networks, and ns-3 and OMNeT++ are two major tools used to model realistic V2X environments. Some of the most important tools used in to simulate mobility and traffic are SUMO, VISSIM and VanetMobiSim. Additionally, there are some tools that allow their interconnection, which enables them to interact with each other in a transparent way, such as VEINS, iTETRIS or VSimRTI.

The first step towards deployment is the choice of the simulation tools. Among all solutions, the most complete and realistic way is through the use of coupled simulators. According to [10], iTETRIS, VEINS and VSimRTI are strong solutions and there is no clear winner, since they all cover the required aspects for VANETs simulation. Despite iTETRIS potential, the project is finished and there is no available support. VEINS simulator already includes a platooning module denominated as PLEXE. However, since VSimRTI is more flexible on the choice of the simulators and allows the use of JAVA programming, the choice falls for VSimRTI. To simulate transportation, the choice is SUMO, since it is able to support detailed representations of large scale traffic scenarios. To support accurate simulation of communication for ITS systems, the choice is ns-3, since it includes all models to reproduce functionalities and protocols for the ITS communications stack. According to [11], a very suitable wireless technology available today to interconnect vehicles is IEEE 802.11p. Since it was specifically built for vehicular environments, it was the technology chosen to allow communication. The intra-platoon distance was based on the values used in [4] for homogeneous vehicles. Another important aspect of this PMP is that maneuvers can happen at any point but only one maneuver is allowed at a time. Allowing more than one maneuver would make the management task extremely complex. Finally, and regarding security concerns, a basic and simple security mechanism for messaging exchanging was implemented. The vehicles are statically assigned one public key pair and one symmetric key and all public keys are pre-shared between the vehicles. The symmetric cipher algorithm used was AES (128 bits key) and for public key scheme it was used a RSA (1024 bits key). The exchanged message is composed of the encrypted payload and the signature. The signature is obtained using SHA-256 and the RSA key.
Fig. 2.

Braga-Porto highways

4.1 Simulation Scenario and Decisions

This subsection describes some important deployment decisions. The selected simulation scenario (illustrated in Fig. 2) area comprises the highways that connect the Portuguese cities of Braga and Porto, obtained from a Open Street Map (OSM) file using the osmosis tool. The vehicles in the simulation that are running the PMP are Trucks, while other vehicles are simple Passenger cars. Additionally, there are some reference Trucks that do not run the PMP, to allow a comparison between them and Platooning-enabled Trucks. The application uses only one of the available ITS-G5 service channels and there are no additional applications running, which implies that there are no congestion problems, nor interferences. To broadcast information on platoons, Leaders use Geocast messages, and V2V Unicast on requests/replies between Leaders and Followers. When receiving a Join Request, the Leader computes a performance value and the position the vehicle should assume on the string based on its performance, sending this information back to the requester. Before starting dismembering the platoon, Leader A goes through a phase of velocity fluctuation, to test the Followers ability to adjust their velocities based on the messages received from the Leader every 100 ms. When the Dissolve process is complete and all vehicles have left platoon A, the simulation reaches its final state. The ns-3 configuration was built to include values that match the communication characteristics of the Cohda MK5 On Board Units (OBUs), as seen on Table 1 to allow more realistic results regarding the communications. In [12], the advised size of the string is 15 vehicles. However, two different sizes were defined for the two different platoon strings created in the simulation (15 and 3 vehicles). Additionally, the two strings travel with different speeds: 20 and 25 m/s, respectively. This happens so that it is easier to test the cases where the string is already full, and to study the impact of the speed on the results. The vehicles running the application are homogeneous, defined with the following parameters: Class - Truck; Maximum Acceleration - 1.1 m/s\(^2\); Maximum Deceleration - 4.0 m/s\(^2\); Maximum Speed - 36.11 m/s; Length - 16.5 m; Width - 2.55 m.
Table 1.

NS-3 WiFi configuration established for Cohda MK5 OBU

Wifi configuration

Wifi Mac


Physical mode


Wifi manager


Received signal energy threshold

−99 dbm

Received signal energy threshold (CCA Busy)

−85 dbm

Transmission gain

10.0 dB

Reception gain

−16.0 dB

Maximum available transmission level

23 dbm

Minimum available transmission level

−10 dbm

Transmission power levels

Step 0.5 dB

Signal-to-Noise-Ratio loss

3.2 dB

5 Results and Analysis

In general, the behavior of the vehicles in ITS applications simulation tends to be extremely dynamic: typically, the mobility simulator runs with hundreds of vehicles equipped with applications at a given penetration rate and they follow computer generated routes. However, the simulation deployment on this work was proposed to be slightly more static, in the sense that the dissolve and leave maneuvers start timings and routes are predefined. Furthermore, the vehicles running the application and the platoon string they should join is also predefined. This causes the lane capacity results to be almost the same from simulation run to simulation run. Still, given the fact that the remaining maneuvers behavior is random, essentially due to the fact that the joining process will cause vehicles to join random positions on the string, independent runs will generate independent results. Although not being the most desired situation, this happens due to the high difficulty level of controlling and evaluating the vehicles and application behavior during the complex maneuvers that result from the PMP. The PMP generates result logs regarding maneuver durations, the exchanged messages, vehicles distance, speed values and lane capacity, which are discussed next.

5.1 Lane Capacity

The first results that can be obtained from the use of Platooning is increased lane capacity. According to [13], the typical lane capacity value is \(C=35\) and the formula to compute it is:
$$\begin{aligned} C = v \times \frac{n}{ns + (n - 1)d + D} \quad \text {vehicles/lane/min,} \end{aligned}$$
where v is the steady state speed (meters/min), d the intra-platoon spacing (meters), D the inter-platoon spacing (meters), s the vehicle length (meters) and n the number of cars composing the string. In the PMP deployment, the maximum theoretical capacity is \(C = 71.88\) vehicles/lane/min. To compare this analytical value to the simulation, the capacity was logged every second and the mean results are presented on Fig. 3. As expected, the capacity is typically lower than the theoretical value, due to the dynamic values of the string (e.g. speed, number of vehicles). Also, these values differ in both platoons, which implies that the capacity values are different between them.
Fig. 3.

Lane Capacity during simulation time

5.2 Maneuvers

This subsection describes the behavior of the Platoons during the application runtime, focusing and discussing on the duration of the maneuvers.

Join Maneuver.

With exception to the Leaders, every vehicle must perform a Join maneuver in order to become part of a platoon. The mean duration of these maneuvers are presented in Table 2. The Waiting value represents the amount of time a vehicle awaits until it receives a Join Acknowledgment after sending a Join Request. The first conclusion one draws is that joining a platoon at the rear is faster than joining a platoon by the side, where other vehicles are required to adjust their gaps, making the maneuver last longer. Still regarding the Join maneuver, it is also important to analyze the duration of a negative response to a Join Request. A reject situation happens when a platoon is already at his maximum size. On average, between the request and response, only 62.2 ms elapse. This happens because the Leader is able to respond almost immediately if a given requester is able (or not) to perform the maneuver, even if another is already occurring.

Leave Maneuver.

In side Leaves, vehicles that follow behind the leaving vehicle are required to open up gaps, so the maneuver is safer. In rear Leaves, the leaving vehicle simply changes lane and leaves. For these reasons, there is a huge difference in the duration of the maneuver, depending on the type: on average, side Leaves last 23.3 s, while rear Leaves last 1.0 s (on the simulator, the operation is immediate, making the result unrealistic).

Adjusting Gaps.

Although these situations are not qualified as maneuvers, they play an important role on their duration times. On average, the Adjust Gap operation lasts 12.6 s on Joins and 17.4 s on Leaves.

Merge Maneuver.

The merge maneuver (exemplified on Fig. 4) can be divided in three steps: exchanging the information between Leaders, the adjusting gap operation and the New Leader information dissemination. On average, the Merge maneuver was accomplished with a similar duration to the operations in Join maneuvers (a Merge operation can almost be seen as one Leader joining another platoon). The adjust gap operation takes most of the time (62.7 s on average) while the maneuver set up and finishing steps are very fast (72.0 ms).


The Dissolve maneuver duration time is estimated from the moment when the Dissolve Request is issued until the last Dissolve Acknowledgment is received. The maneuver performs quickly −3.8 s. This happens because, on the simulator, vehicles acknowledge the request and start manual driving instantaneously, making the result somewhat unrealistic. Hence, these durations only concern the communications part of the maneuver.

Table 2.

Join maneuver mean durations

Join Type

Operation (s)

Waiting (s)

Total (s)













Fig. 4.

Merge maneuver

Fig. 5.

Mean message latency values

Table 3.

Statistic results from messages exchange

Messages latency (ms)





Standard Deviation






Mean Number of Messages


5.3 Messages

The results regarding the delay of the messages are now presented on Table 3 and Fig. 5. The calculated values present a very satisfactory result. They indicate that the PMP is mostly able to deliver the messages on time, and it is able to conform with the communication requirements of the PMP - the messages are able to be generated every 100 ms and the requirement for the maximum delay allowed (100 ms) can be fulfilled. This also means that the impact of the security mechanisms used to encrypt and sign the messages is almost unnoticeable (content is secure and the communication is not compromised). However, there are on average 1073 messages that are delivered with a latency greater than 100 ms. Despite the results not being perfect, the results are still acceptable, since these messages represent a universe of only 0,2%.
Fig. 6.

Vehicle’s distance to go value during simulation

Fig. 7.

Vehicle’s speed deviation during simulation

5.4 Distances and Speed

The Distances log allows the analysis of the distance to go value - the distance towards the computed minimum gap (when positive, the vehicles should accelerate, and vice-versa). Figure 6 illustrates the mean values of the distance to go for all followers in each simulation run (Leaders do not keep distances). These values start to be recorded during the forced fluctuation phase, in order to study the platoon stability. The error bars on the chart represent the standard deviation values. During the speed fluctuating phase, the distance to go is stable, which means that the vehicles can rapidly adjust their speed to reach the correct position based on the information sent by the Leader in the frequent Platoon Group messages. The Speed log also allows the study of the platoon stability during the same fluctuation phase. Figure 7 shows the average of the difference between the actual speed and the required speed for all followers in each simulation run. Vehicles can smoothly adjust their speed to meet the Leader speed. Another important aspect of the speed values that the vehicles assume during the simulation is that they are intimately related to distance to go values. This means that the vehicle must increase its speed when the distance to go value its positive and vice-versa.

6 Conclusions and Future Work

ITS are systems that aim to assure a more efficient and improved usage of the roads by controlling traffic operations and drivers behavior. ITS enables the creation of many applications that use the information from vehicles and infrastructures to implement better driving practices and to improve traffic flow.

This paper discusses related work and general ITS simulation important tools. Then, the PMP is presented, containing the description of the maneuvers, general considerations and some important requirements (based mostly on ITS standards). The second part details the process of deploying the PMP implementation and obtaining the results from the simulation runs. The application was implemented using VSimRTI, coupling SUMO and ns-3. The choice of the tools was not a difficult decision, taking into account that these tools are proven to be very powerful and well-established within the research community. The deployment section describes the simulation scenario and associated decisions as well. The deployment was not an easy task - from the study on the state of the art, it was possible to conclude that the application is immensely complex and sometimes very subjective. For each particular problem that arises from Platooning, there are usually a lot of different proposed solutions. This seems to be an indicator that the Platooning specification is prone to ambiguity. Also, the deployment of the application required a lot of effort to overcome some lack of “intelligence” the chosen tools present - the constant trade-off between a more realistic application and the simulation performance caused some difficulties to evaluate the application behavior.

From the simulation, it is possible to conclude that the PMP works efficiently - maneuvers durations are within an acceptable interval, messages meet the hard communication requirements and the lane capacity is proven to be increased.

Regarding future work, we will consider gathering fuel consumption and emissions information using a more powerful emission model than the open source models available on SUMO. Additionally, we will consider using models that take into account road slopes. Finally, it may be interesting to test the protocol on a real implementation.



This work has been sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion n\(^{\circ }\) 002797/2015 (INNOVCAR 2015–2018), and also by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.


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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Department of Informatics, Algoritmi CenterUniversity of MinhoBragaPortugal

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