A Top-Down Approach to Inter-VehicleCommunication (Poster)

Daniel BaseltDepartment of Computer Science

Heinrich Heine University

Düsseldorf, Germany

Email: baselt@cs.uni-duesseldorf.de

Björn ScheuermannDepartment of Computer Science

University of Würzburg, Germany

Email: scheuermann@informatik.uni-wuerzburg.de

Martin MauveDepartment of Computer Science

Heinrich Heine University

Düsseldorf, Germany

Email: mauve@cs.uni-duesseldorf.de

Abstract—Currently, most of the research on inter-vehiclecommunication uses the same approach as research on general-purpose networks: protocols and algorithms are developed“bottom-up”, i. e., starting from medium access and address-ing, proceeding upwards to the application layer. The set ofapplications is then evaluated through simulation or real-worldexperiments. In this paper we argue that, unlike general-purposenetworks, inter-vehicle communication is about very specific goalsdefined by the application domain: preventing accidents andsaving resources (e. g., travel time, fuel, road capacity). Inter-vehicle communication should therefore be tailored to achievethose specific goals. As a consequence, research in this area shouldfollow a top-down approach, starting out with precise objectives.From these, applications and protocols should be derived—and not the other way around. Besides proposing the generalconcept of top-down research on inter-vehicle communication, weillustrate the process by means of a simple example, showing therelation between available information and road usage efficiency.

I. INTRODUCTION

The exchange of information between cars has the potential

to dramatically improve road safety and traffic efficiency.

Many road accidents could be avoided if the drivers had better

information about the status of other cars and the intentions

of their drivers. For example, if drivers were warned about the

rear end of a traffic jam in time or if a driver approaching an

intersection had information about cross traffic, then road traf-

fic could be much safer. Similarly, if drivers coordinated their

selection of routes and their driving behavior by exchanging

information, then road traffic could be much more efficient.

This insight is not new: it has motivated researchers as

well as car and communication equipment manufacturers to

establish a whole research community over the past decade.

Initially, inter-vehicle communication was driven by the

widespread and low-cost availability of wireless technology

for local (IEEE 802.11) and long-range (UMTS) communica-

tion, combined with the desire to apply this to the exchange

of information between vehicles1. Starting out with available

or slightly modified communication technology [1], the inter-

vehicle communication research community has developed

novel network protocols and distributed algorithms in order to

1There have been several car-to-car oriented research activities before 2000.We limit our discussion here to those that started after suitable wirelesstechnology (i.e., IEEE802.11 and UMTS) became available.

support the exchange of information between vehicles [2], [3],

[4]. These were then used to build applications, such as traffic

information systems [5], [6] or intersection warning assistants

[7]. It is quite likely that the applications developed in this way

will help to avoid accidents and reduce the usage of resources

to some extent.

However, despite these foreseeable benefits, research in

that area today has no idea whether the developed protocols

and applications do really make use of the technology’s full

potential—or whether they are only scratching the surface.

Whichever specific protocols and algorithms are proposed,

questions remain: maybe, if other information was transmitted

between vehicles, we could prevent even more accidents? If

the data exchange took place in a different fashion, might we

be able to reduce the resource consumption even further?

One key reason why we cannot answer these questions

today is that our understanding of the application domain itself

is still very limited. In the area of road traffic modelling and

control there exists related work on how to model the behavior

of vehicles or how to control and optimize traffic flows.

However, inter-vehicle communication makes it possible to

influence (and not just model) the behavior of individual

vehicles (and not just flows). Currently, there exists virtually

no information at all on the interplay between available infor-

mation in the cars, the way the information is exchanged, and

traffic safety and efficiency. We do not know how individual

cars should ideally behave in order to optimize traffic safety

and efficiency in a setting where they are able to communicate

with their environment. Without a solid understanding how

these aspects interrelate, though, any approach to inter-vehicle

communication can be a coarse heuristic at best: we can do

“something” and will likely obtain “some” improvement, but

we cannot be sure that what we pursue is the best (or even just

a reasonably good) way of supporting road traffic by means

of communication.

In some sense, current work on inter-vehicle communication

is therefore similar to finding a good solution to an unspecified

problem—we do actually not know what the desired system

behavior is, but we are nevertheless busy specifying protocols

and applications that aim to achieve it.

In this paper we argue that current research on inter-

vehicle communication should be complemented by a new

2011 IEEE Vehicular Networking Conference (VNC)

978-1-4673-0047-6/11/$26.00 ©2011 IEEE 206

approach. This approach should start out with clear and

concise objectives: minimize the number of accidents and

minimize the resource usage. From these objectives we suggest

to derive how each car should ideally behave. In turn, from

the desired behavior of the cars the information should be

derived that needs to be present in each car in order to achieve

this behavior. This should then allow us to infer algorithms,

protocols, and communication technology choices satisfying

these information needs. In short, instead of continuing to

guess how to improve the current state of the art, we should

acknowledge the fact that we are dealing with a highly

application-specific kind of network, and should therefore start

from the application perspective.

What has mostly been done so far might be termed a

“bottom-up” approach: building the network first, to finally

deal with the application later on. What we propose is a “top-

down” approach to inter-vehicle communication: we advocate

to consider the desired application behavior first, then working

towards a network that supports it in the best possible way.

Developing a top-down approach to inter-vehicle commu-

nication requires the solution of numerous hard and complex

problems. It involves answering questions such as: given a

specific situation, how should each vehicle behave, depending

on the information it has on other vehicles? Or: given that

an interdependency between the information present in a

vehicle and its optimal behavior has been determined, how can

this knowledge be leveraged to design protocols, algorithms,

and communication technology to distribute the information

between vehicles? We cannot hope to address (or even touch)

all these issues in this one paper. Instead, it will require the

long-term effort of many researchers with heterogeneous skills

and backgrounds to successfully develop a comprehensive top-

down approach to inter-vehicle communication. In the paper

at hand, we will therefore only look at an almost trivial setting

in order to provide a first glimpse at how such an approach

might look like.

The remainder of this paper is structured as follows: we will

start out with a brief general roadmap for top-down research

on inter-vehicle communication. Then we will summarize the

key findings of one simple and very specific example of the

top-down approach. The details of how the top-down approach

is applied to this example and what lessons can be learned by

this are discussed next. Finally we discuss related work and

summarize our main points.

II. THE TOP-DOWN ROADMAP

How could a roadmap to top-down research on inter-vehicle

communication look like? We seek to answer this question on

a rather high level of abstraction before delving into the details

of one specific example.

A top-down approach to inter-vehicle communication has

to start with defining the objectives of this technology. Unlike

general purpose networks such as the Internet, the objectives

of inter-vehicle communication are very specific: avoiding

accidents and minimizing resource usage, in particular travel

time, fuel and road capacity.2

Given those objectives we need to understand how road

traffic should look like under the assumption that each vehicle

can exchange arbitrary information with each other vehicle

arbitrarily fast. This will give us an understanding of optimal

road traffic, i. e., a benchmark for all real systems.

In any real system, certainly, vehicles will only have limited

information about each other. The third step of a top-down

approach should therefore be dedicated to the investigation

of the interplay between information availability and vehicle

behavior. This step is all about determining how close to the

optimal road traffic we can get, depending on the specific

information that is available in each vehicle. Further, if the

vehicle is controlled by a human driver, this step needs to

account for reaction time, the limits of human perception, and

the driver’s intentions and interests.

Ideally, at this point we would like to derive optimal

communication patterns and protocols from the results of the

previous step. Yet, it is quite likely that this step will be very

similar to finding an algorithm that solves a given problem,

in that it cannot directly be derived, but additionally requires

human creativity and intelligence to find a solution. In contrast

to the existing situation in inter-vehicle communication, how-

ever, we then have a clear problem statement and can design

solutions accordingly.

Performing a top-down approach in a complex and diverse

setting like the road traffic of a whole city or country in one

pass is likely to result in an unmanageable level of complexity.

We believe that instead the top-down approach will first be

used on specific sections of a road network. I. e., in a first step

it might be applied to a single road or an intersection. As the

experience with the top-down approach increases, the settings

will become more complex. Finally, it might be possible to

scale up to large road networks by combining the findings for

the individual sections that make up the whole road network.

III. RESULTS IN A NUTSHELL

To illustrate the top-down perspective on inter-vehicle com-

munication that we advocate here, we will use a scenario that

is simplified to the point of being almost trivial. This allows

us to focus on the key aspects of this idea. The scenario

encompasses two vehicles, denoted by v1 and v2. They drive

in the same direction on a single-lane road, where they cannot

overtake each other. The first vehicle v1 is driving in front of v2at a constant speed. The only information that v2 has about v1is what is transmitted by v1 via inter-vehicle communication.

We consider the question of how v2 should behave such that

(a) regardless of how v1 proceeds (e. g., even if v1 decided

to suddenly brake) v2 has a sufficient safety distance to react

without crashing into v1, but at the same time (b) the distance

between the two vehicles is minimal. The latter is motivated

2We acknowledge the fact that those goals may be conflicting. In the re-mainder of this paper we simply fix satisfaction levels for all but one objectiveand optimize the remaining one. Obviously, more elaborate approaches areconceivable.

207

by the desire to minimize the usage of road space and thus of

road capacity.

There is clearly a tradeoff here: if fine-grained, detailed, and

frequent information about v1 is provided to v2, then v2 will

be able to follow v1 more closely. The resource “road” can

thus be used more efficiently. However, at the same time, more

network resources must then be spent to deliver that feedback.

If less network resources are used and v2 is, consequentially,

provided with information less often, then a greater safety

distance will be necessary.

If we understand this tradeoff, we are in a position to

argue about how to spend network capacity best in order

to support the application. In particular, we are then able to

compare how well different schemes for information exchange

make use of network capacity. Moreover—and maybe even

more important—we can use the same methodical approach to

determine an ideal “baseline”: assuming that v2 at any point

in time had perfect knowledge about v1, how efficiently could

the road then be used? If a given communication scheme

comes close to the overall optimum obtained with perfect

knowledge and at the same time minimizes communication

requirements, then we know that we have designed a good

inter-vehicle communication scheme not only in relative, but

also in absolute terms.

In this spirit, we shall first make the assumption that v2has perfect information at any time, and argue what this

means for the required safety distance between the vehicles.

Further, we also assume that the future behavior of v1 is

known to be constant (i. e., it continues to drive at a fixed

speed). We will see that v2 will first quickly approach v1and will then soon follow bumper-to-bumper at the same

speed. While the latter is clearly an artifact of the unrealistic

“perfect knowledge” assumption, the resulting behavior of v2nevertheless establishes the comparison baseline: we can then

argue how much we “lose” if we use any given specific, more

realistic communication pattern.

This general approach will allow us to argue about proposed

car-to-car communication protocols in a new way: if a given

approach can be shown to come close to such an optimum

performance limit, then we know that it is a good solution

to reduce accidents and resource usage. If no known protocol

comes close to the derived bounds, then more work needs to

be done—to find better protocols and/or to better understand

the fundamental limitations by deriving tighter bounds on the

achievable performance.

In the latter sense it will of course not suffice to consider

only straightforward and idealized cases. In general, this

will not result in reasonably tight performance bounds. We

therefore continue to narrow the problem down by considering

another extreme case in which v2 receives information about

v1 only once at t0. As a result, v2 needs to be increasingly

“careful”, because its uncertainty about v1’s position and speed

steadily increases after t0. We will argue that, in order to reli-

ably avoid any accident, v2 has to assume the worst possible

case, namely that v1 brakes with maximum deceleration right

after t0.

Based on the foundations established through the discussed

extreme cases, we can then model scenarios in which v2 re-

ceives information on v1 through some arbitrary transmission

scheme. This general case allows us to link the behavior of

the vehicles to the way they communicate with each other.

We will exemplify the use of this knowledge by assessing

a frequently used way of transmitting information between

vehicles: periodic beaconing.

In summary, for the (trivial) setting discussed here, we take

the key step towards a top-down approach: we can evaluate

what a given transmission scheme can accomplish in relative

as well as in absolute terms. Thus we exemplify a first step

from a blind search when looking for an information trans-

mission scheme towards addressing a well-specified problem.

IV. APPLYING THE TOP-DOWN APPROACH

We investigate a scenario consisting of a straight, single

lane starting at position 0 and extending indefinitely. Two

vehicles v1 and v2 enter the scenario. They are punctiform and

cannot overtake each other. The vehicles arrive at position 0 at

times t01 and t02, respectively (w. l. o. g. t01 < t02, i. e., v1 is the

earlier vehicle, driving in front) with given initial velocities.

Their maximum acceleration and deceleration capabilities are

assumed to be identical and independent from their current

speed, and are denoted by A and D, respectively. To keep

things simple and the number of parameters resonable, we

will assume that D = −A.

The progress of a vehicle on the lane is a two times

differentiable (due to the laws of physics) and monotonically

increasing (vehicles are assumed to not reverse direction)

function in time. We call the progress of a vehicle over time

a “way”. In the scenario considered here, we assume that the

way for v1 is always the same: it travels with constant velocity

after entering the lane. Its way can therefore be described

as w1 = w′1(t01) · (t − t01), where w′1(t

01) is the initial (and

subsequently constant) velocity of v1.We are then interested in assessing how v2 should behave

under varying conditions—depending on the initial distance

between the vehicles, their initial velocities, and with different

assumptions about what v2 knows about the vehicle driving

ahead of it. We will start with a situation where v2 has

perfect knowledge about v1 at any time, and will gradually

develop this towards a setting where v2 does only learn about

v1 at discrete points in time—resembling a situation where

beacons are received from v1. This will, in the end, allow

us to understand the impact of the beacon rate on the “safe”

distance between the vehicles, and thus gives an idea of the

interdependency between communication medium usage and

road capacity usage.

In all these cases, we need to consider the possible ways

of vehicle v2. We call a two times differentiable and mono-

tonically increasing function w2 a valid way for vehicle v2if, speaking intuitively, it fits the parameters of that vehicle:

the point in time when the lane is entered and the speed of

the vehicle at that point in time apply, and from this time

on the way does not violate the maximum acceleration and

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deceleration capabilities given by A ≥ 0 and D = −A. More

formally, if v2 arrives at t02, w2 is a valid way for v2 if and

only if w2(t02) = 0 and ∀t ≥ t02 : D ≤ w′′2 (t) ≤ A.

A. The Objective

We say that a valid way w2 for vehicle v2 is accident free if

the order of the vehicles on the lane does never change. That

is, if ∀t : w1(t) ≥ w2(t).3 We only consider scenarios where

at least one accident free way for w2 exists, i. e., where the

initial condition—given by t01, t02, w

′1(t

01), and w′2(t

02)—does

not inevitably lead to an accident. It is easily shown that this

holds if and only if v2 does not change order with v1 if v2starts braking with maximum deceleration immediately when

entering the lane.

Our objective is to minimize the steady-state distance be-

tween both vehicles while guaranteeing that w2 is accident

free. Informally, given a certain algorithm to exchange infor-

mation between vehicles, we seek to understand how closely

v2 can follow v1 in the long run without risking an accident. In

order to answer this question we need to identify the possible

ways for v2 which, given a certain level of knowledge about

v1, are guaranteed to remain accident free. Among all these

ways, we are then interested in the ones that are most efficient

in terms of road space usage. We therefore call an accident-

free way optimal if it allows v2 not to fall behind any other

accident-free way at any point in time.

It is intuitively clear that if v2 has more precise and more

up-to-date information on v1, then it will be able to follow v1more closely without risking an accident. By how much v2 has

to stay behind v1, however, is not intuitively clear. We shall

therefore explore the existence and shape of optimal, accident-

free ways under varying assumptions about v2’s knowledge in

the following subsections.

B. Optimal solution

We first consider the case where v2 is omniscent: it always

knows the precise location and velocity of v1. It also knows

that the velocity of v1 will not change.

The optimal behavior of v2 is then as follows: accelerate

with A (the highest possible acceleration) until a point is

reached where it has to decelerate with D = −A (the

maximum deceleration) so that it will arrive at the same point

as v1, and will drive at the same speed as v1. It will then follow

v1 with that speed. This way for v2 is certainly accident free if

v2 has correct information on the current and future behavior

of v1. Similarly, it is clear that the solution is optimal—no

other accident-free way would allow v2 to be ahead of this

solution at any point in time. The position of v1 and v2 over

time in this setting is visualized in Figure 1.

From these considerations we may draw the following

conclusion:

3We do not consider the case w1(t) = w2(t) to be an accident, since atthis high level of abstraction vehicles do not have a length. This simplifiesthe formal reasoning.

Fig. 1. Behavior of the second vehicle based on perfect knowledge aboutthe first vehicle. ta is the point in time when v2 starts braking. At time tbthe steady-state is reached.

Theorem 1. If v2 continuously knows about v1’s present andfuture position and speed, then there is an optimal, accident-free way for v2 where, after an initial transition period until

tb = te + 2√

Δw(te)A with te = t02 +

w′1(t

02)−w′

2(t02)

A andΔw(te) = w1(t

02) +

A2 (te− t02)

2, v1 and v2 drive at the samespeed and at the same point on the road.

The specific formulae are easily verified by straightforward

calculations from the above made assumptions. Space limita-

tions do not allow us to include detailed proofs here.

Of course, the above result is not very surprising, given the

assumption of perfect knowledge about the present and future

behavior of v1. However, it gives us a baseline for comparison

on how much we lose if v2 does not have that kind of perfect

information.

C. One-Time Information

We now look at another extreme case: information about

the current position and speed of v1 is only available once

when v2 enters the scenario at t02. v2 will no longer know

how v1 behaves afterwards. From this point on, there will be

increasing uncertainty about v1’s whereabouts on the side of

v2. As a consequence v2 will have to be increasingly “careful”

to avoid potential accidents with v1—it needs to take all the

possible future ways of v1 after time t02 into consideration.

It therefore needs to adjust to what could be called the

“worst case” behavior of v1 within this space of possible

ways: braking with maximum deceleration immediately after

v2 entered the scenario and received the information on v1.As before, the optimal behavior of v2 is to begin accel-

erating with acceleration A. Again, it switches to maximum

deceleration at a certain point, which allows v2 to come to a

stop at the same location where the “worst case” way for v1would have made v1 stop. Thereafter, v2 must retain a velocity

of zero—clearly, under the given assumptions, v2 does not

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Fig. 2. Behavior of the second vehicle when information on the first vehicleis received only once. ta is the point in time when v2 starts braking, tbdenotes when the steady state is reached.

know whether it is maybe standing at the same point as v1,and therefore must not proceed any further. The initial strong

acceleration phase before then switching to deceleration yields

a way which is not only guaranteed to be accident free, but is

also optimal in the above defined sense: at any point in time it

is further ahead on the lane than any other accident-free way.

The resulting behavior of v2 is sketched in Figure 2.

We therefore obtain the following theorem:

Theorem 2. If v2 learns about v1’s position and speed onlyonce at time t02, then v2 must come to an halt at time

tb = t02 − w′2(t

02)

A + 2

√w1(t02)

A +w′

1(t02)w

′2(t

02)

A2 + 2t2e with te =w′

1(t02)−w′

2(t02)

2A in order to guarantee accident freeness. Thesteady-state distance between both vehicles therefore increasesunbounded as time passes and v1 proceeds on its way.

Note that the transition period until time tb differs from

Theorem 1, because v2 adapts to the estimated locations and

speeds of a braking v1.

Again, this result is quite evident: v2 cannot follow v1when it is “blind”. Nevertheless, the specific behavior of v2 as

pointed out above provides us important hints on what happens

if v2 receives information not only once, but sporadically from

time to time. So, let us consider this case next.

D. Arbitrary Transmission Schemes

Now, information about v1 is received at v2 through an

arbitrary transmission scheme, i. e., at an arbitrary sequence

of points in time. v1 still drives with constant velocity and

its current state is revealed to v2 whenever v2 receives new

information on v1. In the time between two updates, in order to

guarantee accident freeness, v2 has to assume the worst-case

behavior of v1 as described in the former subsection. After

each update, v2 has more current information on v1 and can

react accordingly. As a result the behavior of v2 will essentially

Fig. 3. Behavior of the second vehicle adapted to arbitrary informationupdates about the first vehicle (received at the solid vertical lines).

be a sequence of maneuvers, each of them being equivalent to

the one-time information case above. Figure 3 illustrates this.

The steady-state distance (if such a steady-state exists at

all), between the vehicles is determined by the respective

transmission scheme. It is a metric to determine the quality

of that scheme with respect to efficient road usage. In the

following section we will look at one specific transmission

scheme and evaluate this metric.

E. Periodic Beaconing

Finally, we consider a scheme for information transmission

that has been frequently proposed for inter-vehicle commu-

nication: periodic beaconing. In this special case v2, ideally,receives an information update periodically, i.e., at time ti, i ∈N0, with ∀ti : ti+1 − ti = const. Again, we assume that v1 is

driving with constant velocity. As in the preceeding subsection

this is not known a priori by v2. After the reception of a

beacon, v2 will once again behave as described for the one-

time information case until the next beacon is received.

The first important question that we need to address is: does

a steady state exist? In fact it can be shown that the following

lemma holds:

Lemma 1. If v2 learns about v1’s position and speed peri-odically at ti, i ∈ N0, with ∀ti : ti+1 − ti = const, then thesequence of distances between both vehicles and the speedsof v2 at the points in time ti converge to a steady state fori→∞.

Figure 4 helps to get an intuitive understanding why this

lemma holds. Each point in the figure stands for one pair

of speed difference (x axis) and position difference (y axis)

at the beginning of a beaconing interval. For each pair of

position and speed differences at one interval, the arrows in

the figure indicate the respective position and speed differences

at the subsequent interval, given that v2 follows the strategy

outlined above. That is, the figure describes how the position

210

Fig. 4. Plot of the velocity difference in relation to the distance between thevehicles at the beginning of an interval with periodic beaconing. The steady

state is at speed difference −AB4

and distance 34Bw′

1(t02) +

AB2

32.

Fig. 5. Behavior of the second vehicle adapted to periodic informationupdates about the first one in the steady state.

and speed differences develop over time, from beacon interval

to beacon interval. For points in the area marked in red on the

right hand side of the figure, it is not guaranteed that v2 can

prevent an accident. It can be seen that, if we start anywhere

outside this zone, then the (speed difference, distance) pairs at

the beginnings of the beacon intervals will approach a steady

state (marked by a green circle), and they will never enter the

“dangerous” zone.

We can turn towards describing how the steady state looks

like. The positions of the vehicles in the steady state are de-

picted in Figure 5. It follows from straightforward calculations

that the following theorem holds.

Theorem 3. The steady state distance between v1 and v2 atthe beginning of each beacon period is given by 3

4Bw′1(t02) +

AB2

32 , where B is the beacon interval length (i. e., the timebetween information updates arriving at v2).

F. Discussion and Further Steps

What does one gain from this simple example? After all, the

setting has been simplified to the point of being almost trivial.

We, ourselves, have learned two important lessons from this

exercise.

Lesson one: for the first time, since we have started

working on inter-vehicle communication, we were able to

reason clearly about design decisions. When talking about

alternative beaconing schemes, i.e., using network coding or

dead-reckoning, we are now able to think about quantifiable

advantages and disadvantages that we derive from clearly

stated goals.

Lesson two: thinking in a top-down fashion has changed our

perspective on inter-vehicle communication. We now consider

more fundamental questions like: “When is transmitted infor-

mation beneficial?”, instead of questions such as: “How well

does our new beacon scheme perform in a simulation/testbed

setting?”.

After understanding the influence of available information

on the behavior of v2, the next task will be to identify the

information dissemination scheme that supports the knowl-

edge requirements best. We would have to switch from the

application level to the protocol layer. From the dissemination

scheme, we then would have to go down further to the lower

layers and eventually decide which kind of physical link is the

right one for this application. This would also mean taking into

account network and channel effects, and also a more realistic

vehicle movement model.

Even though these are all very important aspects of a

top-down approach, they would quite obviously exceed the

limitations of this first paper. So, at this time, we only outlined

a very first step. A full top-down approach in a more complex

setting will be a formidable challenge for a whole research

community. However, meeting this challenge is extremely

rewarding, too, since, in the end, finding the best possible

solution might in fact save lives and protect valuable resources.

V. RELATED WORK

In the area of inter-vehicle communication there has been

been a lot work on protocols. For safety applications, periodic

broadcasting, termed beaconing, is typically used. The beacon-

ing frequency is often assumed to be fixed, e. g., at 10 Hz, as

in [8]. However, a fixed rate enlarges the probability of beacon

collisions in dense vehicle settings, because then the channel

load does not scale [9]. To control the channel load, adaptive

frequency transmission schemes have been proposed, e. g.,

with a focus on fair sharing of bandwidth [10], on estimations

about neighboring vehicles with Kalman filters [11], or on

position error metrics [12].

Designing VANETs with respect to applications and their

security demands is proposed in [13]. However, this work

211

does not explore the information requirements of vehicles

in the same detail as it is done here. In [14], the authors

describe that different safety applications have different update

requirements and thus they propose to adapt sending frequen-

cies accordingly. The importance of considering the worst-

case behavior of neighboring vehicles is discussed in [15].

Exploiting characteristics of the applications of a vehicular

network for creating VANET protocols is not a new idea, as

this is discussed with regard to, e.g., the road topology [16],

the aging of beacons [17], or the speed of vehicles [18]. But

none of these works discussed the impact of the available

information on accident-free driving.

Another specific goal, the road usage improvement with

VANETs, is simulatively evaluated for lane merging in [19].

The work on beaconing schemes will profit significantly

from a top-down approach, since this would enable researchers

to understand the impact of the beaconing scheme on the

specific goals of inter-vehicle communication.

Understanding and modelling the characteristics of vehicles

is important to our approach. Mobility models are a central

research field in the area of vehicular traffic sciences. Many

different driver models have been proposed in the past, includ-

ing [20], [21], [22]. An overview is given by [23]. The models

are intended to describe realistic driving behavior with very

different levels of detail. They are thus of great interest as a

basis for a top-down approach but they do not describe the

interaction between vehicles through communication.

Our approach considers the interdependency between cars.

Similar issues have been discussed in the area of automated

driving. During the last two decades, several projects worked

on the vision of driverless cars using inter-vehicle communica-

tion, e.g. EUREKA Prometheus [24] and PATH [25]. The latter

discusses communication-enabled applications and a control

system architecture but not the protocols directly.

A system in which physical, real-world components like

vehicles interact with computation and digital communication

is also sometimes referred to as a cyber-physical system [26].

The authors of [27] discuss the influence of vehicular move-

ments on inter-vehicle communication, but do not close the

loop back to the impact of information availability on vehicle

behavior, as done in our work.

The model proposed in this paper is very simple com-

pared to the highly complex dynamics of vehicles and traffic

scenarios with more vehicles and advanced topologies. For

understanding more sophisticated scenarios, tools from the

area of control theory are expected to be of good use.

A subarea considered to be of interest is optimal control

theory, which can be used to describe how to determine

vehicle trajectories that optimize given objective functions

(see, e. g., [28], [29]). Another subarea is cooperative control

which allows for modeling the communication and interaction

between vehicles [30]. In the PATH project, the control of

highway platoons has been analyzed [31]. Though also inter-

vehicle communication was considered in, e.g., [32] and [33],

parameters like delays and sending frequencies are chosen

rather arbitrarily (instead of analytically). We examine the

interplay of the communication and behavior to learn about

how a good protocol can be obtained.

In [34], a multi-vehicle system with connectivity constraints

for monitoring an area under objective functions like minimiz-

ing the final time is discussed. No prior work, however, has

suggested to structure the research process on inter-vehicle

communication itself in a top-down fashion.

VI. CONCLUSIONS

In this paper we have introduced the idea of top-down

research in the area of inter-vehicle communication. We ar-

gued that, in contrast to communication in general purpose

networks, the exchange of data between vehicles has clearly

defined goals: preventing accidents and optimizing resource

usage. Thus, research in this area should derive applications,

protocols and algorithms from those very specific goals, in-

stead of building the network first and then consider potential

functionality that could be achieved with this network. We

provided a first glimpse on how this top-down process could

look like by examining an almost trivial example. This is only

a tiny first step on a very challenging path that will require

the effort of many research groups with very heterogeneous

skills. However, due to the goals—preventing accidents and

saving valuable ressources—this effort is likely to be very

much worthwhile.

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