Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model
The creation of protocols for autonomous intersection management is an active research topic with the potential of increasing the capacity of intersections addressing the increasing demand on roads. Most of the proposed protocols assume that all the vehicles involved will behave pro-socially, that i...
- Autores:
-
Morales Chavarro, Javier Mauricio
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/79380
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
Autonomous vehicles
Internet of vehicles (IoV)
deceiving agents
Traffic model
Autonomous intersection
Vehículos autónomos
Internet de los vehículos
Agentes engañosos
Modelo de tráfico
Intersección autónoma
Inteligencia artificial
Artificial intelligence
Programación informática
Computer programming
Automatización
Automation
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| title |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| spellingShingle |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Autonomous vehicles Internet of vehicles (IoV) deceiving agents Traffic model Autonomous intersection Vehículos autónomos Internet de los vehículos Agentes engañosos Modelo de tráfico Intersección autónoma Inteligencia artificial Artificial intelligence Programación informática Computer programming Automatización Automation |
| title_short |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| title_full |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| title_fullStr |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| title_full_unstemmed |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| title_sort |
Analyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational model |
| dc.creator.fl_str_mv |
Morales Chavarro, Javier Mauricio |
| dc.contributor.advisor.none.fl_str_mv |
Niño Vásquez, Luis Fernando |
| dc.contributor.author.none.fl_str_mv |
Morales Chavarro, Javier Mauricio |
| dc.contributor.educationalvalidator.none.fl_str_mv |
Colman, Ewan |
| dc.contributor.researchgroup.spa.fl_str_mv |
LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas Autonomous vehicles Internet of vehicles (IoV) deceiving agents Traffic model Autonomous intersection Vehículos autónomos Internet de los vehículos Agentes engañosos Modelo de tráfico Intersección autónoma Inteligencia artificial Artificial intelligence Programación informática Computer programming Automatización Automation |
| dc.subject.proposal.eng.fl_str_mv |
Autonomous vehicles Internet of vehicles (IoV) deceiving agents Traffic model Autonomous intersection |
| dc.subject.proposal.spa.fl_str_mv |
Vehículos autónomos Internet de los vehículos Agentes engañosos Modelo de tráfico Intersección autónoma |
| dc.subject.unesco.none.fl_str_mv |
Inteligencia artificial Artificial intelligence Programación informática Computer programming Automatización Automation |
| description |
The creation of protocols for autonomous intersection management is an active research topic with the potential of increasing the capacity of intersections addressing the increasing demand on roads. Most of the proposed protocols assume that all the vehicles involved will behave pro-socially, that is, in a way that improves the outcome of the system over their individual gain. We simulated three different autonomous intersection protocols, two centralized and one decentralized, introducing some egoistic agents that we call deceiving vehicles. Deceiving vehicles may decide to transmit false information while using a protocol if they detect that doing so can result in a lower delay in the intersection. Our simulations show that in two of the protocols, it is possible for a deceiving vehicle to experience lower delay times compared to its non-deceiving counterparts. Additionally, as more deceiving vehicles enter the system the overall capacity of an intersection can be reduced, increasing delays for non-deceiving vehicles which creates an incentive for more vehicles to deceive. We pose that, given that vehicles have an incentive to deceive, autonomous intersection protocol's authors need to consider deceiving vehicles in their design and include measures to prevent them, thus avoiding the performance degradation they produce. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-04-06T17:33:33Z |
| dc.date.available.none.fl_str_mv |
2021-04-06T17:33:33Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/79380 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional UN |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/79380 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional UN |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Al-qutwani, M. and Wang, X. (2019). Smart traffic lights over vehicular named data networking. Information (Switzerland), 10(3). Aoki, S. and Rajkumar, R. (2019). V2V-based synchronous intersection protocols for mixed traffic of human-driven and self-driving vehicles. In Proceedings - 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019, Electrical and Computer Engineering, Carnegie Mellon University, United States. Ashtiani, F., Fayazi, S. A., and Vahidi, A. (2018). Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming. In Proceedings of the American Control Conference, volume 2018-June, pages 6341-6346, Clemson University, Department of Mechanical Engineering, Clemson, SC29634-0921, United States. IEEE. Azimi, R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2012). Intersection management using vehicular networks. SAE Technical Papers. Azimi, R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2013a). V2Vintersection management at roundabouts. SAE International Journal of Passenger Cars - Mechanical Systems, 6(2):2013-01-0722. Azimi, R., Bhatia, G., Rajkumar, R. R., and Mudalige, P. (2014). STIP: Spatio-temporal intersection protocols for autonomous vehicles. In 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pages 1-12, Carnegie Mellon University, United States. IEEE. Azimi, S. R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2013b). Reliable intersection protocols using vehicular networks. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, ICCPS 2013, pages 1-10, Carnegie Mellon Universtiy, General Motors Company, United States. ACM Press. Azimi, S. R., Bhatia, G., Rajkumar, R. R. R., and Mudalige, P. (2011). Vehicular Networks for Collision Avoidance at Intersections. SAE International Journal of Passenger Cars - Mechanical Systems, 4(1):406-416. Bentjen, K., Graham, S., and Nykl, S. (2018). Modelling misbehaviour in automated vehicle intersections in a synthetic environment. In Proceedings of the 13th International Conference on Cyber Warfare and Security, ICCWS 2018, volume 2018-March, pages 584-593, Air Force Institute of Technology, Dayton, United States. Bento, L. C., Parafita, R., and Nunes, U. (2012). Intelligent traffic management at intersections supported by V2V and V2I communications. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pages 1495-1502. IEEE. Bifulco, G. N., Caiazzo, B., Coppola, A., and Santini, S. (2019). Intersection crossing in mixed traffic flow environment leveraging V2X information. In 2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings, Architectural and Environmental Engineering (DICEA), University of Naples Federico II, Department of Civil, Naples, 80125, Italy. Buckman, N., Pierson, A., Schwarting, W., Karaman, S., and Rus,D. (2019). Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation. In IEEE International Conference on Intelligent Robots and Systems, pages 6136-6143, Massachusetts Institute of Technology, CSAIL, Cambridge, MA, United States. Buzachis, A., Celesti, A., Galletta, A., Fazio, M., and Villari, M. (2018). A secure and dependable multi-Agent autonomous intersection management (MAAIM) system leveraging blockchain facilities. In Proceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018, pages 189-194, Department of Mathematics and Computer Science, Physics and Earth Sciences, University of Messina, Viale F. Stagno D'Alcontres 31, Messina, 98166, Italy. Chen, L. and Englund, C. (2016). Cooperative Intersection Management: A Survey. IEEE Transactions on Intelligent Transportation Systems, 17(2):570- 586. Dresner, K. and Stone, P. (2008). A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 31:591- 656. Faggella, D. (2020). The Self-Driving Car Timeline - Predictions from the Top 11 Global Automakers. Fontes, R. D. R., Campolo, C., Rothenberg, C. E., and Molinaro, A. (2017). From Theory to Experimental Evaluation: Resource Management in Software- Defined Vehicular Networks. IEEE ACCESS, 5:3069-3076. Gao, Q., Fu, C., Wang, J., Liu, Y.-H., and Deng, W.-W. (2013). Vehicle active scheduling model at intersection. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 43(6):1638-1643. González, C. L., Zapotecatl, J. L., Gershenson, C., Alberola, J. M., and Julian, V. (2019). A robustness approach to the distributed management of traffic intersections. Journal of Ambient Intelligence and Humanized Computing. Jiménez, F., Naranjo, J. E., Anaya, J. J., García, F., Ponz, A., and Armingol, J. M. (2016). Advanced Driver Assistance System for Road Environments to Improve Safety and Efficiency. In Transportation Research Procedia, volume 14, pages 2245-2254. Khoury, J., Khoury, J., Zouein, G., and Arnaout, J.-P. (2019). A practical decentralized access protocol for autonomous vehicles at isolated under-saturated intersections. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 23(5):427-440. Khoury, J. J. and Khoury, J. J. (2014). Passive, decentralized, and fully autonomous intersection access control. In 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, pages 3028-3033, Faculty of Civil Engineering, Lebanese American University, Byblos, Lebanon. IEEE. Kiela, K., Barzdenas, V., Jurgo, M., Macaitis, V., Rafanavicius, J., Vasjanov, A., Kladovscikov, L., and Navickas, R. (2020). Review of V2X-IoT standards and frameworks for ITS applications. Applied Sciences (Switzerland), 10(12). Kim, K.-D. (2013). Collision free autonomous ground traffic: A model predictive control approach. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, ICCPS 2013, pages 51-60. Kim, K.-D. and Kumar, P. R. (2015). An MPC-based approach to provable system-wide safety and liveness of autonomous ground traffic. IEEE Transactions on Automatic Control, 59(12):3341-3356. Kneissl, M., Molin, A., Esen, H., and Hirche, S. (2018). A Feasible MPC-Based Negotiation Algorithm for Automated Intersection Crossing . In 2018 European Control Conference, ECC 2018, pages 1282-1288, Corporate R and D Department, DENSO Automotive Deutschland GmbH, Freisinger Str., 21-23, Eching, 85386, Germany. Li, H. and Tiwari, R. (2018). Safe and reliable spatiooral model for roundabouts and road intersections using vehicular communication system. In Proceedings of 2018 16th International Conference on Intelligent Transport System Telecommunications, ITST 2018, School of Engineering, Newcastle University, Newcastle-upon-Tyne, United Kingdom. Li, H., Zhang, J., Zheng, F., Li, L., and Ran, B. (2018). Autonomous and Connected Vehicles: The Capacity of Mixed Traffic Flow at Signalized Intersection with the ACDA-MTD Model. In CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals, pages 34-45, Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast Univ., Si Pai Lou #2, Nanjing, 210096, China. Li, Z. R., Chitturi, M. V., Yu, L., Bill, A. R., and Noyce, D. A. (2015). Sustainability effects of next-generation intersection control for autonomous vehicles. Transport, 30(3):342-352. Lima, A., Rocha, F., Völp, M., and Esteves-Verissimo, P. (2016). Towards safe and secure autonomous and cooperative vehicle ecosystems. In CPS-SPC 2016 - Proceedings of the 2nd ACM Workshop on Cyber-Physical Systems Security and PrivaCy, co-located with CCS 2016, pages 59-70. Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y. P., Hilbrich, R., Lucken, L., Rummel, J., Wagner, P., and Wiebner, E. (2018). Microscopic Traffic Simulation using SUMO. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-Novem:2575-2582. Makarem, L. and Gillet, D. (2012). Information sharing among autonomous vehicles crossing an intersection. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2563-2567. IEEE. Mendoza, M. G., Holloway, B., Hevia, A., Céspedes, S., and Bustos, J. (2017). Coordination of autonomous vehicles at intersections with decentralized V2V communication. In CEUR Workshop Proceedings, volume 1950, pages 38-41, NIC Chile Research Labs, Chile. Mirnig, A. G., Stadler, S., and Tscheligi, M. (2017). 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Intersection intelligence: Supporting urban platooning with virtual traffic lights over virtualized intersection-based routing. Sensors (Switzerland), 18(11). Savic, V., Schiller, E. M., and Papatriantafilou, M. (2017). Distributed algorithm for collision avoidance at road intersections in the presence of communication failures. In IEEE Intelligent Vehicles Symposium, Proceedings, pages 1005-1012, Dept. of Computer Science and Engineering, Chalmers University of Technology, Sweden. IEEE. Sawade, O. and Radusch, I. (2016). Survey and classification of cooperative automated driver assistance systems. In 2015 IEEE 82nd Vehicular Tech- nology Conference, VTC Fall 2015 - Proceedings. Tang, X., Li, M., Lin, X., and He, F. (2020). Online operations of automated electric taxi fleets: An advisor-student reinforcement learning framework. Transportation Research Part C: Emerging Technologies, 121(February):102844. Teoh, E. R. (2020). What's in a name? Drivers' perceptions of the use of five SAE Level 2 driving automation systems. Journal of Safety Research, 72:145-151. Vaio, M. D., Falcone, P., Hult, R., Petrillo, A., Salvi, A., and Santini, S. (2019). Design and Experimental Validation of a Distributed Interaction Protocol for Connected Autonomous Vehicles at a Road Intersection. IEEE Transactions on Vehicular Technology, 68(10):9451-9465. Wei, H., Mashayekhy, L., and Papineau, J. (2018). Intersection Management for Connected Autonomous Vehicles: A Game Theoretic Framework. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, volume 2018-Novem, pages 583-588, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, United States. Wooldridge, M. (2012). Computation and the prisoner's dilemma. IEEE Intelligent Systems, 27(2):75-80. Wu, T.-Y. T.-Y., Guizani, N., and Hsieh, C.-Y. C.-Y. (2016). An efficient adaptive intelligent routing system for multi-intersections. Wireless Communications and Mobile Computing, 16(17):3175-3186. Wu, W., Liu, Y., Xu, Y., Wei, Q., and Zhang, Y. (2017). Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment. Journal of Sensors, 2017:1-6. Zheng, B., Sayin, M. O., Lin, C.-W., Shiraishi, S., and Zhu, Q. (2017). Timing and security analysis of VANET-based intelligent transportation systems: (Invited paper). In IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, volume 2017-Novem, pages 984-991, University of California, Riverside, CA, United States. |
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Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación |
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Departamento de Ingeniería de Sistemas e Industrial |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeMorales Chavarro, Javier Mauricioe058c62eaace500c3d7b142bdb220b8cColman, EwanLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI2021-04-06T17:33:33Z2021-04-06T17:33:33Z2021https://repositorio.unal.edu.co/handle/unal/79380Universidad Nacional de ColombiaRepositorio Institucional UNhttps://repositorio.unal.edu.co/The creation of protocols for autonomous intersection management is an active research topic with the potential of increasing the capacity of intersections addressing the increasing demand on roads. Most of the proposed protocols assume that all the vehicles involved will behave pro-socially, that is, in a way that improves the outcome of the system over their individual gain. We simulated three different autonomous intersection protocols, two centralized and one decentralized, introducing some egoistic agents that we call deceiving vehicles. Deceiving vehicles may decide to transmit false information while using a protocol if they detect that doing so can result in a lower delay in the intersection. Our simulations show that in two of the protocols, it is possible for a deceiving vehicle to experience lower delay times compared to its non-deceiving counterparts. Additionally, as more deceiving vehicles enter the system the overall capacity of an intersection can be reduced, increasing delays for non-deceiving vehicles which creates an incentive for more vehicles to deceive. We pose that, given that vehicles have an incentive to deceive, autonomous intersection protocol's authors need to consider deceiving vehicles in their design and include measures to prevent them, thus avoiding the performance degradation they produce.La creación de protocolos de manejo autónomo de intersecciones es un tema de investigación activo que tiene el potencial de aumentar la capacidad de las intersecciones aportando a la solución del problema del creciente aumento en la demanda en las vías. La mayoría de los protocolos propuestos asumen que todos los vehículos se comportan de manera prosocial, es decir, que actúan de una manera que beneficia al sistema sobre su propio beneficio. Nosotros simulamos tres protocolos de intersección autónomos, dos centralizados y uno descentralizado, introduciendo algunos agentes egoístas que llamamos vehículos engañosos. Los vehículos engañosos pueden decidir transmitir información falsa cuando usan un protocolo si detectan que hacerlo puede resultar una demora menor en la intersección. Nuestras simulaciones muestran que, en dos de los protocolos, es posible que los vehículos engañosos experimenten demoras menores frente a sus contrapartes no-engañosos. Asimismo, conforme más vehículos engañosos son introducidos en el sistema, la capacidad total de la intersección se ve reducida, aumentando las demoras para los vehículos que no son engañosos lo que genera un incentivo para que más vehículos sean engañosos. Proponemos que, dado que los vehículos tienen incentivos para engañar, los autores de protocolos de intersecciones autónomas deben considerar los vehículos engañosos en su diseño e incluir medida para prevenirlos, evitando así la degradación en rendimiento que producen.MaestríaSistemas Inteligentes1 recurso en línea (47 páginas)application/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::003 - SistemasAutonomous vehiclesInternet of vehicles (IoV)deceiving agentsTraffic modelAutonomous intersectionVehículos autónomosInternet de los vehículosAgentes engañososModelo de tráficoIntersección autónomaInteligencia artificialArtificial intelligenceProgramación informáticaComputer programmingAutomatizaciónAutomationAnalyzing the Effect of Deceiving Agents in a System of Self-Driving Cars at an intersection - a computational modelTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAl-qutwani, M. and Wang, X. (2019). Smart traffic lights over vehicular named data networking. Information (Switzerland), 10(3). Aoki, S. and Rajkumar, R. (2019). V2V-based synchronous intersection protocols for mixed traffic of human-driven and self-driving vehicles. In Proceedings - 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019, Electrical and Computer Engineering, Carnegie Mellon University, United States. Ashtiani, F., Fayazi, S. A., and Vahidi, A. (2018). Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming. In Proceedings of the American Control Conference, volume 2018-June, pages 6341-6346, Clemson University, Department of Mechanical Engineering, Clemson, SC29634-0921, United States. IEEE. Azimi, R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2012). Intersection management using vehicular networks. SAE Technical Papers. Azimi, R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2013a). V2Vintersection management at roundabouts. SAE International Journal of Passenger Cars - Mechanical Systems, 6(2):2013-01-0722. Azimi, R., Bhatia, G., Rajkumar, R. R., and Mudalige, P. (2014). STIP: Spatio-temporal intersection protocols for autonomous vehicles. In 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pages 1-12, Carnegie Mellon University, United States. IEEE. Azimi, S. R., Bhatia, G., Rajkumar, R., and Mudalige, P. (2013b). Reliable intersection protocols using vehicular networks. In Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, ICCPS 2013, pages 1-10, Carnegie Mellon Universtiy, General Motors Company, United States. ACM Press. Azimi, S. R., Bhatia, G., Rajkumar, R. R. R., and Mudalige, P. (2011). Vehicular Networks for Collision Avoidance at Intersections. SAE International Journal of Passenger Cars - Mechanical Systems, 4(1):406-416. Bentjen, K., Graham, S., and Nykl, S. (2018). 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