Improving Collision Detection in Distributed Virtual Environments byAdaptive Collision Prediction Tracking

Jan OhlenburgFraunhofer Institute Applied Information Technology FIT

Sankt Augustin, Germanyjan.ohlenburg@fit.fraunhofer.de

Abstract

Collision detection for dynamic objects in distributedvirtual environments is still an open research topic. Theproblems of network latency and available network band-width prevent exact common solutions. The Consistency–Throughput Tradeoff states that a distributed virtual envi-ronment cannot be consistent and highly dynamic at thesame time. Remote object visualization is used to extrap-olate and predict the movement of remote objects reducingthe bandwidth required for good approximations of the re-mote objects. Few update messages aggravate the effect ofnetwork latency for collision detection.

In this paper new approaches extending remote object vi-sualization techniques will be demonstrated to improve theresults of collision detection in distributed virtual environ-ments. We will show how this can significantly reduce theapproximation errors caused by remote object visualizationtechniques. This is done by predicting collisions betweenremote objects and adaptively changing the parameters ofthese techniques.

1. Introduction

In distributed virtual environments multiple users inter-act with each other or with world objects in real–time. Usu-ally, these users are represented by an avatar in the virtualworld. When navigating through the world or interactingwith objects the other users are notified of the user actionby visual, sensory or audio feedback, e.g. they see how theavatar of that user walks or interacts with objects. To ensurethis awareness, messages have to be exchanged between allusers. How these messages are distributed among the usersis an important factor and depends on the underlying frame-work of the distributed virtual environment, e.g. [2][4][14].These frameworks are different in terms of scalability, net-work architecture and network communication.

The properties of the frameworks have great impact onhow and when messages are distributed among the users.The most important factor for collision detection is networklatency. Collision detection is usually performed betweena pair of virtual objects. In non-distributed virtual envi-ronments the position and the orientation of both objectsis known at the time of the collision query. This is not thecase in distributed virtual environments. Collision detec-tion between two moving avatars can hardly be performedwith the exact positions and the exact orientations of bothobjects. Due to network latency, all update messages arrivesome time after the message has been sent. Therefore, atthe time of the collision query the position and the orienta-tion could have changed, since another update message hasnot arrived yet. A fundamental rule about distributed vir-tual environment’s shared state describes this drawback. Itis known as the Consistency–Throughput Tradeoff [18]:

”It is impossible to allow dynamic sharedstate to change frequently and guarantee that allhosts simultaneously access identical versions ofthat state.”

The Consistency–Throughput Tradeoff states, that a dis-tributed virtual environment cannot support both dynamicbehavior and absolute consistency. To proof this statement,two scenarios are described, the first consistent and theother highly dynamic.

In a consistent distributed virtual environment no clientmay change the state of the environment unless all otherclients have agreed to the new state. Suppose, a user wantsto change his avatar’s position and suffers from 100 ms net-work latency. It takes at least 100 ms until all other clientshave received this request and another 100 ms to send anacknowledgement back to user. 200 ms of round–trip timeto change the state of the environment means that no morethan 5 consecutive state changes can be processed per sec-ond. Interaction is therefore limited to 5 frames per second,which is far away from supporting dynamic behavior.

83IEEE Virtual Reality 2004 March 27-31, Chicago, IL USA 0-7803-8415-6/04/$20.00©2004 IEEE.

Proceedings of the 2004 Virtual Reality (VR’04) 1087-8270/04 $ 20.00 IEEE

In a highly dynamic distributed virtual environmentusers may change the state of objects without the agreementof other users. Inconsistencies are handled after they haveoccurred. Suppose, a user is walking around in the environ-ment, at each frame he sends update messages to all otherusers about his current position, e.g. current position is p.At the time this update message arrives at the other clients,these clients only know that the user is somewhere near p,since in the mean time his position could have been changedagain. As long as users are changing the state of the envi-ronment, no consistent state will be reached.

Section 2 provides an overview about the previous work.The following section, Section 3, explains the experimentwhich has been used to demonstrate the new approachesfor collision detection in distributed virtual environmentsand approaches for remote object visualization, Sections 4and 5. Additionally, the topic of decision resolution will bediscussed in Section 6. At the end of the paper the workwill be concluded and an outlook into future research topicsis given in Section 7.

2. Previous Work

The distributed virtual environment NPSNET [19] whichhas the focus on human–computer interaction and softwaretechnology for implementing large–scale virtual environ-ments is specialized in military maneuver simulation andhas a simple and limited collision detection and collisionresponse. The collision detection process is divided intothree levels. In the first level the object’s height above theterrain is compared to the maximum height of all objectswithin close proximity. If the object is not higher than thetallest objects, the process continues with the second level.Here the distances between the objects are computed. Ifthe distance is smaller than the cylinder radii, then a col-lision has occurred. In this case the process moves on tothe third phase — the resolution phase — where the col-lision response is computed, the collision response is lim-ited to the situation that objects involved either stop or die[15]. The NPSNET Research Group has not implementedthe collision detection especially for distributed virtual envi-ronments, that is to say, no special strategy has been appliedto handling problems encountered in distributed collisiondetection. But, as many other distributed virtual environ-ments, it utilizes dead reckoning which is a technique forremote object visualization.

Sandoz and Sharkey [16] are discussing the problem ofnetwork latency and provide an example where network la-tency has no side effects. In this example — a distributedsnooker game — only the event that the cue ball has beenhit by a player is sent. Every participant performs collisiondetection locally and calculates the positions of the snookerballs independently.

Similarly, in a virtual tennis match [11][12] physicallaws can be utilized to predict the movement of the tennisball. The ball’s position can be exactly predicted in spite ofhigh latencies. The user, who has to return the ball, can de-termine the position of the ball exactly and additionally heknows the exact positions of his racket as well. Thereforehe is able to perform exact collision detection.

These two approaches apply to all object movementfollowing strict rules (usually physical laws). For unpre-dictable movement, e.g. a squirrel running around, othertechniques have to be utilized. The following approachesare able to predict the movement of object by extrapolatingthe movement of the past.

Dead reckoning, used by NPSNET [15], predicts the po-sition and orientation of remote objects by using the lastknown position and orientation, together with the positionalvelocity and angular velocity to calculate the actual positionand orientation. Second–order dead reckoning also uses thepositional and angular accelerations. The same dead reck-oning algorithm is executed at the local and the remote hostfor each dead reckoned object. Therefore, the local hostknows the approximation error of the remote host. Twothresholds are used to decide whether to send a new updatemessage to the remote host: an error–based threshold anda time–based threshold. If the approximation error exceedsthe error–based threshold, a new update message is sent tothe remote host, while the time–based threshold specifiesthe maximal time between two consecutive update mes-sages. Usually both thresholds are used at the same time.On the arrival of a new update message, the dead reckoningprocess has to decide whether to jump to the actual posi-tion, or to calculate a smooth path to a predicted positionin the near future. The smooth back method reduces visualinconsistencies, but this can result in the object chasing itscorrect location. By utilizing dead reckoning the networktraffic can be reduced by two orders of magnitude, whilestill having good approximations of remote objects.

A more complex solution to extrapolating the positionsand orientations of remote objects is the position history–based protocol proposed by Singhal and Cheriton [17]. Likedead reckoning, the local host executes the tracking algo-rithm as well to be aware of the approximation error. In-stead of using the object’s positional and angular veloci-ties and accelerations, the tracking algorithm uses the lastthree positions and orientations of an object. The algorithmadapts to the object’s behavior by differentiating betweensharp turns and smooth motion. A sharp turn in the object’smovement is recognized by calculating the angle betweenthe three most recent update positions. If the angle is small,a sharp turn is assumed; otherwise smooth motion is as-sumed. In the case of a sharp turn, the two most recentpositions are used to extrapolate subsequent positions. Inthe case of a smooth motion, second–order tracking is ap-

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plied. The convergence point is adaptively determined de-pending on the movement, and also the convergence path,which is either parabolic (second–order) or linear (first–order). First–order convergence is applied if the angle be-tween the three most recent positions is large, indicatingalmost linear movement; otherwise second–order conver-gence is utilized. Singhal and Cheriton have compared theirapproach with dead reckoning used by NPSNET. They ob-served that the position history–based protocol requires lessbandwidth in performing at least as well as dead reckoningfor smooth object motion and potentially better for non–smooth motion.

Multiplayer Network Games normally utilize centralizedarchitectures, where the centralized server performs all statechanges. The client’s game state logic only predicts the re-sult of the server. More information about multiplayer net-work games and how they deal with the problems of net-work latency can be found in [1].

Object scene remote enslavedscene none Remote none

CollisionPrediction

remote Remote Remote AdaptiveCollision Collision CollisionPrediction Prediction Prediction

Trackingenslaved none Adaptive none

CollisionPredictionTracking

Table 1. Overview of applied approaches fordistributed collision detection.

3. Experimental Outline

The following approaches apply mainly to non-centralized network architectures. However, they apply toClient/Server network architectures as well, if the serveris used only to distribute the messages of the clients; butdoes not perform collision detection. Additionally, these ap-proaches do not rely on a special collision detection system,they are able to decide which host has to perform the col-lision query and improve the collision detection results byreducing the approximation errors of the involved objects.For detailed information about collision detection systemssee [3][5][8][7][9][10].

Virtual objects of a distributed virtual environment aredivided into scene objects, enslaved objects and remote ob-jects. Scene objects are static or their movement is prede-

fined, i.e. each host knows their exact position and orienta-tion at any time. Enslaved objects are moved by the localuser (e.g. the avatar), update messages have to be sent to allother hosts to ensure, that they become aware of the move-ment. Remote objects are enslaved on a remote host (e.g.the avatar of another participant). The position and orien-tation of remote objects is only approximated because ofremote object visualization techniques and network latency.

The interesting object pair collisions are scene–remote,remote–remote and remote–enslaved, because the positionand the orientation of at least one involved object is approx-imated.

Table 1 shows the applied approaches for the differentobject pairs; none means that no approach is applied, onlycollision detection is executed.

If collisions have to be detected between scene and re-mote objects or between two remote objects, the local clientis not included in the decision whether a collision has oc-curred or not. Until the message arrives indicating a colli-sion or a change of direction, collisions of remote objectsshould be predicted. This is done by the method of remotecollision prediction (see Section 4).

The hardest challenge is the detection of collisions be-tween remote and enslaved objects. No client has exact in-formation about all objects involved. To reduce the approx-imation errors induced by dead reckoning, the approachadaptive collision prediction tracking is utilized (see Sec-tion 5).

-40pi15 sec

-35pi13.125 sec

-30pi11.25 sec

-25pi9.38 sec

-20pi7.5 sec

-10pi3.75 sec

0-4-202468

z

x

Scene Objects

Remote Objects

Figure 1. Test scenario

For the following experiments the dead reckoning tech-nique has been used. The results can be applied to the po-sition history–based protocol as well, since they basicallywork the same way. The main difference would be that theposition history–based protocol approximates smooth mo-tions better than dead reckoning.

3.1. The Test Scenario

To demonstrate the effects and the efficiency of the twoapproaches described below, the following test scenario hasbeen chosen, see Figure 1.

An enslaved object is moved from z = 0 to z = −40πin 15 seconds. The value for x is determined by Equation 1

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and y = 0.

x =

−z6 ; |z| < 10π

z+20π6 ; 10π < |z| ≤ 20π

|5 sin( z5 )| ; 20π < |z|

(1)

Collision with scene objects occur at z = 10π andz = (20 + 5i)π, where i = 0 . . . 4. Remote objects arelocated at (8, 0, 27.5π), (8, 0, 32.5π) and (8, 0, 37.5π), butno collisions with these remote objects occur. The enslavedobject is 5.42m wide.

The test scenario combines smooth motion and sharpturns, which are two common motion classes [17]. Thethird motion class is random motion. Since tracking andconvergence algorithms are of little benefit for this kind ofmotion, this motion class is not tested with this scenario.

The reason, why random motion is not examined here,is that this type of motion is usually not the result of a col-lision. More likely, it is the result of collision avoidance,e.g. while someone walks through a crowd of people hewill try to avoid running into someone else by changing di-rection, velocity and acceleration very often. Nonetheless,the two approaches, described below, apply to random mo-tion as well, since they do not make assumptions about thetype of motion. While remote collision prediction will beof little use for random motion, the adaptive collision pre-diction tracking will have comparable results, since the areaof interest can be made large enough so that more updatemessages are able to approximate the motion.

In Section 6 the problem of decision resolution is out-lined and exemplary solutions are provided.

All diagrams of the experiments for this test scenario canbe found in [13], where the results of the experiments havebeen provided for different latencies.

4. Remote Collision Prediction

Dead reckoning has its weaknesses, after a collision hasoccurred, it takes some time for this update message to ar-rive at remote hosts. Sharp turns, e.g. as a result of a colli-sion, cannot be predicted by the dead reckoning technique,see Figure 2. Therefore, a client has to predict collisions ofa remote object on bases of the current approximated objectposition.

The approach is straight forward. As stated before, ifthe local client is not included in the collision detection be-tween two objects, because both are remote or one is remoteand the other is static, remote collision prediction should beutilized. Collision detection between these two objects isperformed with their approximated positions and velocities,but the result is not distributed to other hosts. If a collisionoccurs an approximated collision response is calculated andused until the next update message arrives.

By applying remote collision predicting, the worst ap-proximation errors have been eliminated. The collision attime t = 3.75 is predicted correctly and the approximationerror is independent of network latency. The remaining ap-proximation error is due to the different frame rates of thetwo clients, collisions are detected at different times andtherefore with different object positions. The error is neg-ligible, since the predicted path and the visual appearanceare correct. Even though the collision response for the col-lision at time t = 7.5 is wrong, remote collision predictionis able to reduce the approximation error by more than 20%and at the time the next update message arrives, the pre-dicted position is less error prone. In Figure 3 the resultsof the experiments, where remote collision prediction wasbeing applied, can be seen at t = 3.75, t = 7.5, t = 9.38,t = 11.25 and t = 13.125.

The first two collisions could have been detected withcorrect remote object positions, because the remote object’smovement had been straight. This is not the case for the col-lisions at time t = 9.38, t = 11.25 and t = 13.125. As thediagrams show, the remote object’s position is error proneat the time of collision. In the test scenario remote colli-sion prediction is able to reduce the approximation error toa minimum because the collision response is the same onboth clients. Of course, this approach would fail if no col-lision would have been detected at the remote host, in thiscase the path would have stayed the same until the next up-date message arrives.

The most important benefit of remote collision predic-tion is that it prevents remote objects from entering otherobjects.

5. Adaptive Collision Prediction Tracking

The reduction of network traffic is achieved by using anerror–based threshold, as mentioned before, update mes-sages for enslaved objects are only sent if the error of thepredicted position exceeds the error–based threshold. Thismechanism implies larger error of remote object positions;the minimal error only depends on network latency. Sincethe network bandwidth is a limited resource, a trade–off hasto be found between high update rates to support collisiondetection and low network traffic to save bandwidth.

The following approach is able to compensate low up-date rates at times where collisions between remote and en-slaved objects are very unlikely for the near future. Thisis done by calculating the time of collision for two movingobjects. If this time is below a threshold, the parameters forthe dead reckoning protocol are adapted to achieve higherupdate rates, i.e. the error–based threshold is reduced to asmall value.

Definition 1 Two objects are in close proximity if their en-closing bounding spheres intersect, or if they will intersect

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0 3.75 7.5 9.38 11.25 13.125 15−5

0

5

10

Simulation time [sec]

Obj

ect’s

cen

ter

posi

tion

[m]

0 3.75 7.5 9.38 11.25 13.125 150

1

2

3

4

5

Simulation time [sec]

Err

or o

f rem

ote

obje

ct’s

pos

ition

[m]

0 3.75 7.5 9.38 11.25 13.125 150

1

2

3

4

Simulation time [sec]

Out

put r

ate

[KB

yte/

sec]

True Motion of Enslaved ObjectShadowed Motion of Remote ObjectUpdate Message

Figure 2. Test scenario at 200 ms network la-tency with dead reckoning.

or have intersected within a certain time ∆τ . ∆τ is calledclose proximity threshold.

To test whether two objects, P and Q, are in close prox-imity, it is checked whether their bounding spheres intersectat time t = 0. If they do, no further calculations have tobe performed. Otherwise, the time at which the boundingspheres have contact is calculated with their current posi-tion and current velocity. The position of object P and Qat time t = δ is calculated by Equation 2 and Equation 3respectively. The distance of both objects at time t = δis the length of the vector

−→dδ (Equation 4), which is calcu-

lated by Equation 5. The bounding sphere is used for thisapproach, since it is the only bounding volume, which isrotation invariant. Due to this property, the distance of twobounding spheres can be described by a closed formula. Allother bounding volumes cannot be used for this approach.

0 3.75 7.5 9.38 11.25 13.125 15−5

0

5

10

Simulation time [sec]

Obj

ect’s

cen

ter

posi

tion

[m]

0 3.75 7.5 9.38 11.25 13.125 150

1

2

3

4

5

Simulation time [sec]E

rror

of r

emot

eob

ject

’s p

ositi

on [m

]

0 3.75 7.5 9.38 11.25 13.125 150

1

2

3

4

Simulation time [sec]

Out

put r

ate

[KB

yte/

sec]

True Motion of Enslaved ObjectShadowed Motion of Remote ObjectUpdate Message

Figure 3. Test scenario with remote collisionprediction and adaptive collision predictiontracking at 200 ms latency.

The disadvantage of the bounding sphere that it fits objectsvery poorly is an issue for this approach, e.g. a wall 10 mhigh, 1 m thick and 100 m long has a bounding sphere witha radius of 50.25 m. No matter which direction an object ismoving towards this wall the close proximity starts at latestwhen entering the sphere, see Figure 4.

−→Pδ = −→

P0 + −→vP · δ (2)

−→Qδ = −→

Q0 + −→vQ · δ (3)

−→dδ = −→

Q0 −−→P0 (4)

distanceδ =√−→

dδ · −→dδ (5)

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distanceδ = r ⇔ (6)

distanceδ2 = r2 ⇔

−→dδ · −→dδ = r2

To determine the time where the bounding spheres of ob-ject P and Q have contact, Equation 5 is set equal to thesum of both radii, r. The calculation is simplified by squar-ing both sides of the equation, i.e. squared distance equal tor2.

Bounding Sphere

Wall

Figure 4. 2D view of close proximity example.Although the object path is far away from thewall, this is a close proximity situation, dueto the poor approximation of the boundingsphere.

Solving Equation 6 yields either one, two or no result forδ. No result means, the bounding spheres do not intersectat their current velocities, neither in the past nor in the fu-ture. Otherwise, there is at least one result. Let δmin bethe minimum of the two results and δmax the maximum, inthe case of one result δmin = δmax. δmin and δmax canboth be negative, a negative value indicates that the timeof contact was in the past. If δmin or δmax is negative thedouble of the absolute value is assigned to them. This ap-proach declares two objects within close proximity even ifthe time of contact was in the past. The reason for that isexplained by an example of the test scenario. In the test sce-nario, the enslaved object avoids collisions with objects attime t = 10.315, t = 12.185 and t = 14.06, after the en-slaved object has reached its turning–point and moves intothe opposite direction the collision has been avoided. De-pending on network latency, the object position at the re-mote client has not reached its turning–point yet. To ensurethat the remote object follows the path of the enslaved ob-ject smoothly, the adaptive collision prediction tracking re-quires for high update rate some time after a collision hadbeen avoided. Since the movement towards another objectis more critical than the movement away, negative values aremultiplied by −2, i.e. half of the close proximity threshold.

The two objects are in close proximity if the value ofmin(δmin, δmax) is less than the close proximity threshold,τ .

The efficiency of this approach can be spotted in Fig-ure 3, where the approaches of remote collision predictionas well as adaptive collision predicting tracking have beenapplied. At time t = 8.44 no obstacle has to be avoided,therefore this approach does not have to be applied. Attimes t = 10.315, t = 12.185 and t = 14.06 the approachnotices that the enslaved object comes into close proxim-ity and uses a different error–based threshold. The resultingaverage error is significantly smaller than without applyingadaptive collision prediction tracking. Of course the net-work traffic is increased during close proximity situations,but the achieved improvements justify this extra traffic.

Since an application can not only choose the error–basedand time–based threshold to find a trade–off between net-work traffic and exact remote object visualization for eachdead reckoned object, but also an error–based threshold forclose proximity situations, it is now possible that this trade–off does not aggravate the collision detection results.

6. Decision Resolution

When two enslaved objects collide, it is very likely thatthe two hosts compute different collision results. As exam-ined in the test scenario, only if one of the objects’ move-ments is straight or does not move at all, the remote side isable to predict its position exactly. The approximation errorfor turns becomes larger the faster an object moves.

Unless the distributed virtual environment uses aClient/Server architecture, where the collision detection isonly performed at the server, there has to be a strategy todecide, which of the hosts’ result is chosen to be correct(even if it is not).

This decision has to be made independently on differenthosts and the result must be the same. On detecting a col-lision a host needs to know whether it may distribute theresult or not, to prevent the distribution of different mes-sages for the same event. E.g. host A detects a collisionof its enslaved object OA with the remote object OB en-slaved by host B, host A calculates the collision responseand distributes the results. Host B has concluded for thesame situation that no collision has occurred and thereforewill send a message of its new position. Other hosts willnot be able to decide which message is correct and will endup in an inconsistent state. The process of choosing a hostto be responsible for the collision detection will be nameddecision resolution.

The easiest way to make such a decision is to use thehosts’ unique identification number. The host with thelower identification number, A, will be responsible for de-tection collisions and distributing the results. The other

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host, B, knows that it has the higher identification num-ber and therefore will not perform collision detection, evenif this means that the enslaved object enters the other ob-ject’s geometry. Since if host A does not detect a collisionit will only send update messages of its object and not amessage that no collision has occurred. Otherwise, if hostB would perform e.g. remote collision prediction this couldlead to an inconsistent state, because no resynchronizationmessage for its object is sent. Owing to the simplicity ofthis method any other criterion will probably produce betterresults.

6.1. Velocity–Based Decision Resolution

To produce better collision detection results more com-plex solutions have to be considered. First of all, the maxi-mal approximation error is derived. If no dead reckoning isused or used with an error–based threshold of 0, the maxi-mal approximation error is distmax = |velocity · latency|.distmax is the distance an object can move until the mes-sage arrives at the remote host at the current velocity. Ofcourse, this implies that the velocity is constant, otherwisethe maximal error is distmax = |velocity · latency + 0.5 ·acceleration · latency2|. I.e. the higher the latency and/orthe velocity is, the bigger the approximation error will be.

This fact can be utilized for deciding which host is re-sponsible for collision detection, since the velocity of re-mote objects is known. If the host of the object with thehigher velocity is chosen the collision detection result willbe in general more exact, since the other host will have ahigher approximation error for the object. There are somedrawbacks of this method, the type of movement is nottaken into account and due to network latency, different ve-locities for the same object can exist at the two hosts. There-fore, if the velocity is chosen as the criterion, a velocity attime t = t0 −∆t has to be used, where t0 is the time of thedecision and ∆t has to be greater than any possible networklatency. Thus this criterion can only be used for reasonablevalues of ∆t. To take the type of motion into account, aposition history has to be used, such as used in the positionhistory–based protocol. The usage of ∆t applies as well.

6.2. Field of View–Based Decision Resolution

One reason, why better collision detection results are de-sirable, is that visual inconsistencies should be limited. In adistributed virtual environment with low latencies (less than10 ms) and slow moving objects, e.g. with maximal velocityof 10 m/s then distmax = 10cm and the environment willbe resynchronized after a collision within 10 ms. In thisscenario the simple approach from above will have the de-sired effect, tolerable inconsistencies and no noticeable vi-sual inconsistencies. This is not the common case; therefore

a criterion for decision resolution can be the reduction of vi-sual inconsistencies. Since collision detection is performedwith the information about the enslaved and the remote ob-ject of the deciding host, the user will not suffer from visualinconsistencies. Therefore, the field of view of the users canbe used. E.g. in a distributed racing game, the player in thecar in front, A, only sees the other car of player B throughhis rear–view mirrors. Collisions with his rear–side and thefront–side of the following car should be performed on thebasis of the information residing at the host of player B. Italso has to be ensured that both host are doing the decisionresolution on the same data, therefore the field of view attime t = t0 − ∆t has to be used.

7. Conclusion and Future Work

We showed how the results of collision detection in dis-tributed virtual environments can be significantly improvedby utilizing the approaches Remote Collision Prediction andAdaptive Collision Prediction Tracking. They easily inte-grate into systems utilizing remote object visualization, in-cluding dead reckoning and the position history–based pro-tocol, and the experimental results stated their efficiency.We showed that remote collision prediction can lead to bet-ter results of remote object visualization by applying somesimple rules. The approach adaptive collision predictiontracking is able to find a trade–off between high update ratesto support collision detection and the reduction of networktraffic. This was done by distributing update messages athigher rates only if two enslaved objects are in close prox-imity and a collision becomes more likely. We also showedthat the approach of adaptive collision prediction tracking isable to reduce the approximation error to a minimum whenrequired.

Additionally, we outlined the problem of decision reso-lution and provided criteria to solve this problem.

In our future work, we will be looking into the followingresearch topics.

The collision detection in distributed virtual environ-ments determines the close proximity by utilizing bound-ing spheres. Poorly approximated objects have a large closeproximity area and therefore raise the required bandwidthusage unnecessarily. Subdivision of the object to provide abetter approximation of an object to reduce the close prox-imity area should be very helpful.

Also, collisions between remote objects, which are notin the field of view, do not have to be detected by eachclient. A client becomes aware of the collision by updatemessages. Therefore, a strategy to decide for which objectscollision detection has to be performed should be added tothe system.

Quality of Service (QoS), which is part of the new inter-net standard IPv6, is able to provide a known upper–bound

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on latencies and a guaranteed bandwidth [6]. With this ser-vice completely new approaches are possible, since theseapproaches can rely on fixed circumstances and thereforeare able to ignore possible network congestions.

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