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Autonomous Agents and Multi-Agent Systems
Erschienen in: Autonomous Agents and Multi-Agent Systems 5/2015

01.09.2015

Affordance-based agent model for road traffic simulation

verfasst von: Feirouz Ksontini, René Mandiau, Zahia Guessoum, Stéphane Espié

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 5/2015

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Abstract

Existing traffic simulations often consider normative driver behavior. Drivers do not always use physically delineated lanes: sometimes drivers use the entire road surface. Thus, current traffic simulations do not reproduce all observed urban and suburban traffic phenomena. To improve the validity of urban and suburban traffic simulations, we propose to consider driving context and driver behavior in terms of occupied space. We endow driver agents with an ego-centered representation of the environment based on the concept of affordances and virtual lanes. Affordances thus identify the possible space occupation actions afforded by the environment and by other agents. The proposed model was implemented using our ArchiSim tool. We show that this model is more efficient and realistic than existing models. The experiments also reproduce real traffic situations and compare simulated data to real data.
Vorheriger Artikel Efficient solutions for joint activity based security games: fast algorithms, results and a field experiment on a transit system
Nächster Artikel Reusing cost-minimal paths for goal-directed navigation in partially known terrains

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Fußnoten
1
The French National Institute for Transport and Safety Research (ex-INRETS).
 
2
The distance to the norm (traffic rules) is randomly specified during agent initialization; it denotes the degree of compliance with norms and ensures heterogeneity for the agents.
 
3
This function choice is completely empirical: we chose the parameters that affect agent behavior based on psychological studies.
 
4
The expected agent speed in lane \(VV_{j}\) is given by the weighted sum of the parameters mentioned below.
 
5
French acronym for “The Environment and Energy Management Agency”
 
Literatur
1.
Allen, J. (1981). An interval based representation of temporal knowledge. In: Proceedings of the seventh International Joint Conference on Artificial Intelligence, (pp 221–226).
2.
Bazzan, A. (2005). A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, 10(1), 131–164.CrossRef
3.
Bazzan, A., & Klügl, F. (2014). A review on agent-based technology for traffic and transportation. The Knowledge Engineering Review, 29(3), 375–403.
4.
Bonte, L., Espié, S., & Mathieu, P. (2006). Modélisation et simulation des usagers deux-roues motorisés dans archisim. In: Actes des14e Journées Francophones sur les Systèmes Multi-Agents (JFSMA’06), (pp 31–44).
5.
Bonte, L., Espié, S., & Mathieu, P. (2007). Virtual lanes interest for motorcycles simulation. In Proceedings of the fifth European Workshop on Multi-Agent Systems, (pp 580–596).
6.
Chen, B., & Cheng, H. (2010). Review of the application of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems, 11(2), 485–497.CrossRef
7.
Cohn, A., & Renz, J. (2008). Qualitative spatial representation and reasoning. In F. van Harmelen, V. Lifschitz, & B. Porter (Eds.), Handbook of knowledge representation (Vol. 3, pp. 551–596). Amsterdam: Elsevier.CrossRef
8.
Cornwell, J., O’Brien, K., Silverman, B., & Toth, J. (2003). Affordance theory for improving the rapid generation, composability and reusability of synthetic agents and objects. In: Twelfth Conference on Computer Generated Forces and Behavior Representation.
9.
Dai, J., & Li, X. (2010). Multi-agent systems for simulating traffic behaviors. Chinese Science Bulletin, 55, 293–300.CrossRef
10.
Doniec, A., Mandiau, R., Piechowiak, S., & Espié, S. (2008). Anticipation based on constraint processing in a multi-agent context. Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 17(2), 339–361.CrossRef
11.
El Hadouaj, S., Drogoul, A., & Espié, S. (2000). How to combine reactivity and anticipation : the case of conflicts resolution in a simulated road traffic. In S. Moss & P. Davidsson (Eds.), Multi-Agent-Based Simulation, Second International Workshop (pp. 82–96). Boston: Springer.CrossRef
12.
Espié, S. (1995). Archisim, multi-actor parallel architecture for traffic simulation. In: Proceedings of the Second World Congress on Intelligent Transport Systems, Yokohama, vol IV.
13.
Fellendorf, M., & Vortisch, P. (2010). Microscopic traffic flow simulator vissim. In J. Barceló (Ed.), Fundamentals of Traffic Simulation, International Series in Operations Research and Management Science (Vol. 145, pp. 63–93). New York: Springer.
14.
Gerevini, A., & Nebel, B. (2002). Qualitative spatio-temporal reasoning with rcc-8 and Allen’s interval calculus: Computational complexity. In F. van Harmelen (Ed.), Proceedings of the 15th European Conference on Artificial Intelligence, ECAI’2002 (pp. 312–316). Lyon, France: IOS Press.
15.
Gibson, J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting and knowing, Lawrence Erlbaum and associates. New Jersey: Hillsdale.
16.
Gipps, P. (1981). A behavioural car-following model for computer simulation. Transportation Research Part B, 15(2), 105–111.CrossRef
17.
Hidas, P. (2002). Modelling lane changing and merging in microscopic traffic simulation. Transportation Research Part C, 10(5–6), 351–371.CrossRef
18.
Kapadia, M., Singh, S., Hewlett, W., & Faloutsos, P. (2009). Egocentric affordance fields in pedestrian steering. In: Proceedings of the 2009 symposium on Interactive 3D graphics and games, ACM, I3D ’09, (pp 215–223).
19.
Ksontini, F., Espié, S., Guessoum, Z., & Mandiau, R. (2012a). Traffic behavioral simulation in urban and suburban - representation of the drivers’ environment. In: Advances in Intelligent and Soft Computing, PAAMS, vol 155, (pp 115–125). Springer.
20.
Ksontini, F., Guessoum, Z., Mandiau, R., Espié, S. (2013). Using ego-centered affordances in multi-agent traffic simulation. In M.L. Gini, O. Shehory, T. Ito, C.M. Jonker (eds) International conference on Autonomous Agents and Multi-Agent Systems, AAMAS ’13, (pp 151–158) Saint Paul, MN, USA, May 6–10, 2013, IFAAMAS.
21.
Lee, T., Polak, J., & Bell, M. (2009). New approach to modeling mixed traffic containing motorcycles in urban areas. Transportation Research Record, 2140, 195–205.CrossRef
22.
Lewis-Evans, B., & Charlton, S. G. (2006). Explicit and implicit processes in behavioural adaptation to road width. Accident Analysis & Prevention, 38(3), 610–617.CrossRef
23.
Li, S., & Ying, M. (2003). Region connection calculus: Its models and composition table. Artificial Intelligence, 145(1–2), 121–146.MathSciNetCrossRef
24.
Mandiau, R., Champion, A., Auberlet, J. M., Espié, S., & Kolski, C. (2008). Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Applied Intelligence, 28(2), 121–138.CrossRef
25.
Minh, C. C., Sano, K., & Matsumoto, S. (2005a). Characteristics of passing and paired riding maneuvers of motorcycle. Journal of the Eastern Asia Society for Transportation Studies, 6, 186–197.
26.
Minh, C. C., Sano, K., & Matsumoto, S. (2005b). The speed, flow and headway analyses of motorcycle traffic. Journal of the Eastern Asia Society for Transportation Studies, 6, 1496–1508.
27.
Murphy, R. (1999). Case studies of applying gibson’s ecological approach to mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 29(1), 105–111.CrossRef
28.
Norman, D. (1999). Affordances, conventions and design. Interactions, 6(3), 38–43.CrossRef
29.
Papasimeon, M., Pearce, A., & Goss, S. (2007). The human agent virtual environment. In: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, (pp 1–8). Honolulu, Hawaii, AAMAS ’07.
30.
Rao, A. S., & Georgeff, M. P. (1991). Deliberation and its role in the formation of intentions. In B. D’Ambrosio & P. Smets (Eds.), Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 300–307). Los Angeles, CA, USA: Morgan Kaufmann.
31.
Rao, A. S., & Georgeff, M. P. (1995). Bdi agents: From theory to practice. In V. R. Lesser & L. Gasser (Eds.), Proceedings of the First International Conference on Multiagent Systems (ICMAS) (pp. 312–319). San Francisco, California, USA: The MIT Press.
32.
Raubal, M. (2001). Ontology and epistemology for agent-based wayfinding simulation. International Journal of Geographical Information Science, 15(7), 653–665.CrossRef
33.
Saad, F. (1992). In-depth analysis of interactions between drivers and the road environment: contribution of on-board observations and subsequent verbal report. In: Proceedings of the 4th Workshop of ICTCT, University of Lund.
34.
Six, L., Guessoum, Z., Saunier, J., & Ieng, S.S. (2013). Towards a truck-driver model using a hysteresis based analysis and verification approach. In M. L. Gini, O. Shehory, T. Ito, & C. M. Jonker (Eds.) proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS’2013), IFAAMAS, (pp 1219–1220).
35.
Stoytchev, A. (2005), Behavior-grounded representation of tool affordances. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), (pp 3071–3076).
36.
Tornros, J. (1998). Driving behavior in a real and a simulated tunnel: A validation study. Accident Analysis & Prevention, 30, 497–503.CrossRef
37.
Vasirani, M., Klugl, F., Camponogara, E., & Hattori, H. (Eds.) (2012). Proceedings of the 7th International Workshop on Agents in Traffic and Transportation (ATT), AAMAS 2012, Valencia, Spain.
38.
Wang, H., Kearney, J. J., & Cremer, Willemsen P. (2005). Steering behaviors for autonomous vehicles in virtual environments. In Proceedings of the IEEE Virtual Reality Conference (pp. 155–162). Germany: Bonn.
39.
Weyns, D., & Holvoet, T. (2005). On the role of environments in multi-agent systems. Informatica, 29(4), 409–421.
40.
Weyns, D., Schumacher, M., Ricci, A., Viroli, M., & Holvoet, T. (2005). Environments in multiagent systems. Knowledge Engineering Review, 20(2), 127–141.CrossRef
41.
Weyns, D., Omicini, A., & Odell, J. (2007). Environment as a first class abstraction in multiagent systems. Autonomous Agents and Multi-Agent Systems, 14(1), 5–30.CrossRef
42.
Xia, L., & Li, S. (2006). On minimal models of the region connection calculus. Fundamenta Informaticae, 69(4), 427–446.MathSciNet
43.
Zieba, S., Polet, P., & Vanderhaegen, F. (2011). Using adjustable autonomy and human–machine cooperation to make a human–machine system resilient—application to a ground robotic system. Information Sciences, 181(3), 379–397.CrossRef
Metadaten
Titel
Affordance-based agent model for road traffic simulation
verfasst von
Feirouz Ksontini
René Mandiau
Zahia Guessoum
Stéphane Espié
Publikationsdatum
01.09.2015
Verlag
Springer US
Erschienen in
Autonomous Agents and Multi-Agent Systems / Ausgabe 5/2015
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-014-9269-x

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