Integration of Road Network Logics into Virtual Environments1Tobias Haubrich∗ 1Sven Seele† 1,2,3Rainer Herpers† 1Peter Becker†

Institute of Visual Computing, Bonn-Rhein-Sieg University of Applied Sciences1

University of New Brunswick2, York University3

ABSTRACT

Underlying semantics are important to facilitate traffic simulationsin virtual environments. The work presented here introduces anuniversal and extensible model for the representation of road net-work logics. Additionally, setup processes of road network logicsfollowing the model are described, and the integration of differenttraffic simulation approaches is discussed. For evaluation, scenesaccording to different scenarios were created and semantics wereintegrated. Results indicate significant time savings from automaticgeneration of road network logics.

Index Terms: I.2.11 [Artificial Intelligence]: Distributed ArtificialIntelligence—Intelligent Agents I.6.8 [Simulation and Modeling]:Types of Simulation—Gaming

1 INTRODUCTION

One important step in creating immersive virtual environments(VE) is to establish simulations of various entities (e.g. in formof virtual agents) which populate a scene and thus breathe life intoit. To achieve realistic agent behavior, agents need to gather infor-mation from their VE. To simplify the process of information gath-ering and thus to facilitate perception processes of virtual agents,semantics need be integrated into the virtual environment. In ourspecific application, these semantics are provided by road networklogics in the form of a road network layer integrated into existingvirtual scenes. This network layer is not visible to a user, but servesas a platform to agents for navigation and information retrieval. Thelogics can provide information about properties of a road itself orabout virtual objects along the road. In the case of driving agents,the integration of such a logical network is possible, because driv-ing agents are typically not able to move freely throughout the en-tire environment. Instead, they are more or less constrained to astatic traffic network.

2 SERONET: A MODEL FOR ROAD NETWORK LOGICS

A formal model for the description of road networks is desired toprovide a consistent basis for simulation. Since no formal model forthe description of road networks exists which fulfills our needs – ca-pable of including simulations of different accuracy – a new modelwas defined, called SeRoNet (Semantic Road Network Model). Ex-isting models are usually too specialized in terms of the appliedsimulation approach. The model is inspired by the model used inthe VISSIM Simulator [4] and by the way roads are built in real-ity. In [5] a first version of the SeRoNet model was introduced andan approach for generating real-life road network data in form ofOpenDRIVE data was presented.

In our definition, a road network is a 7-tuple RN =(L,C,R,P, I,N,J) comprised of a set of lanes (L), a set of connec-

∗e-mail: tobias.haubrich@inf.h-brs.de†e-mail: {sven.seele, rainer.herpers, peter.becker}@h-brs.de

tors (C), a set of roads (R), a set of paths (P), a set of information(I), a set of infonodes (N), and a set of junctions (J).

The road network model itself is based on a graph represen-tation. There are two types of edges: lanes and connectors. Alane (l ⊆ N) represents one lane of a road whereas a connector(c = (N , lpre, lsuc)) represents one possible way through a junc-tion. Each end of a lane is adjacent to a set of connectors, but aconnector has exactly one preceding (lpre) and exactly one succeed-ing lane (lsuc). Either a lane or a connector can be referred to as asegment. Each segment also contains an ordered sequence (N ) ofinfonodes (n = ((x,y),F )) from N. These describe the segment’sgeometry and features (F ⊆ I) at particular spots of the segment(e.g. changes in lane width or traffic signs).

To structure the graph representation and provide another levelof abstraction, four more network elements are introduced. Road(r) and path (p) subsume a set of lanes or connectors respectively.Additionally, junctions ( j = (C ,F )) subsume a set of connectors(C ) belonging to a junction’s area and a set of features (F ) from Idescribing the junction.

3 SETUP OF ROAD NETWORK LOGICS

An intuitive approach to set up road network logics might be a man-ual setup as shown in figure 1. This can be facilitated by the im-plementation of intuitive editing tools (cf. [1]). Nevertheless, theoverhead of a manual setup does not scale well with growing roadnetworks. Thus, we pursued an automatic setup of the logics. Anautomatic generation process which uses standardized OpenDRIVEdata [3] as an input was realized. OpenDRIVE provides several ad-vantages while offering a solid foundation for future development.

4 RUNNING SIMULATIONS ON SERONET

Simulation approaches with different levels of complexity were in-tegrated into the model for simulating a large number of vehiclespersistently throughout the road network. The first approach is amesoscopic approach based on a queueing system (cf. [2]) whichis integrated in the road and junction elements. Vehicles are movedthrough the network in form of packets containing information that

Figure 1: Part of a virtual scene including a junction. A semanticrepresentation of the traffic network is provided by integrated roadnetwork logics. The cubic elements are infonodes. Sequences ofinfonodes form segments. Additionally, the relation between trafficlights and infonodes can be observed at the borders of the junction.

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Scenario Region Region OpenDRIVE # of # of # of # of Generation Estimated manualDimensions file size Lanes Connectors Junctions Infonodes time setup time

1 Siegburg 1 km×0.65 km 1,85 MB 254 492 63 2902 1,84s 2540min / 42,33h2 Sankt Augustin 2 km×1.5 km 3,49 MB 573 950 154 6012 4,63s 5730min / 95,5h3 VG Hachenburg 10 km×5 km 14,18 MB 2017 3575 563 23902 62,14s 20170min / 336,17h

Table 1: Properties and average generation times of the road network logics for three evaluation scenarios. The manual setup times areestimated by assuming that the creation of one lane with its preceding and succeeding connectors takes 10 minutes.

needs to be preserved. In a specific vicinity around a user, the simu-lated vehicles are transferred to a microscopic simulation level pro-viding realistic traffic. If a vehicle enters this vicinity, the embodi-ment of the vehicle is restored and a basic agent model is attached.The agent-controlled vehicles travel strictly along the infonodes un-til their full functionality, including simulations of physics, percep-tion and personality is activated.

Due to the design of the proposed SeRoNet model, it is also pos-sible to integrate simulation logics of other simulation approaches.For example, consider a simulation based on cellular automata (cf.[6]). To apply this approach, segments of the SeRoNet could be di-vided into cells as suggested in [1]. Lane changing can be includedby updating vehicles with different lane change directions in dif-ferent time steps to avoid collisions [7]. Junction areas could besimulated by allowing vehicles only in cells of a single connector.

5 EVALUATION

For evaluation purposes, three scenarios were defined (see table 1).Corresponding OpenDRIVE data was generated using the work-flow presented in [5]. A total of five virtual scenes were created.Three of them (one for each scenario) were generated using theTrian3D Builder software. Two additional virtual scenes of sce-nario 1 were constructed by different procedures. One using theEsri CityEngine and one by a manual creation. The same shape-file data was used as input for both tools. The manually modeledscene is based on satellite images. Automatically generated roadnetwork logics were fit into the Trian3D Builder scenes without ad-justments. This result was expected since the software producedthe OpenDRIVE data. A fitting of the logics into the other sceneswas also achieved. Some minor adjustments in selected areas werenecessary to fit logics into the Esri CityEngine scene. Due to devi-ation by manual setup, an inclusion in the manually created sceneneeded adjustments of e.g. junction positions. Finally, all sceneswere successfully populated with agents.

A second evaluation focused on the time savings due to the au-tomated generation process. By considering the manual creationof lanes of varying complexity we empirically estimated an aver-age time of up to 10 minutes for the creation of a single lane with

Figure 2: Automatically generated road network logics according toscenario 3. The logics were generated in about one minute. The timeestimated for a manual setup is 8 working weeks.

its connectors. This includes the correct connection to all adjacentconnectors and addition of necessary information. Using this av-erage time, we calculated an estimate of the time it would take tomanually create logics for the three sample scenarios neglecting anyerrors caused by a human designer. Additionally, the duration of 10generation processes per scenario were logged and averaged. Thegeneration was performed on a consumer-level desktop computer(Intel Core 2 Quad Q6700 processor, 4GB RAM). Table 1 lists thegeneration times and estimated times for a manual creation as wellas the properties of the scenarios. The automatic generation processis capable of building large scenarios within only a few minutes; ahuman would presumably need several weeks. Furthermore, theautomatic process does not only save time, it also avoids mistakesdue to imprecisions. Additionally, if revisions (e.g. changes of themodel) are necessary, the entire network can be regenerated at once.

6 CONCLUSIONS AND FUTURE WORK

A model for the description of road network logics was introducedwhich allows storing of various information within a virtual scene.The model also allows the integration of different simulation ap-proaches. Using an automated generation process, the setup oflogics is feasible even for large areas. To improve the generationprocess and thus the road network logics, more features (e.g. eleva-tion) could be included. More qualitative input data would furtherimprove the results of the generation process. For final refinementsof the logics, intuitive editing tools are required. Current researchfocuses on agent-central perception processes including virtual sen-sors for evaluation of the road network logics.

ACKNOWLEDGEMENTS

The AVeSi project is being funded by the FHprofUnt program ofthe BMBF (grant 17028X11).

REFERENCES

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