Abstract
Autonomous vehicles will most likely participate in traffic in the near future. The advent of autonomous vehicles allows us to explore innovative ideas for traffic control such as norm-based traffic control. A norm is a violable rule that describes correct behavior. Norm-based traffic controllers monitor traffic and effectuate sanctions in case vehicles violate norms. In this paper, we present an extension of SUMO that enables the user to apply norm-based traffic controllers to traffic simulations. In our extension, named TrafficMAS, vehicles are capable of making an autonomous decision on whether to comply with norms. We provide a description of the extension, a summary on its implementation and demonstrative experiments.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdelkader G (2003) Requirements for achieving software agents autonomy and defining their responsibility. In: Proceedings of the autonomy workshop at AAMAS, vol 236
Ackerman E (2015) Tesla working towards 90 percent autonomous car within three years. http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/tesla-working-towards-90-autonomous-car-within-three-years. Accessed 29 June 2015
Alechina N, Dastani M, Logan B (2012) Programming norm-aware agents. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems-volume 2, International foundation for autonomous agents and multiagent systems, pp 1057–1064
Alechina N, Dastani M, Logan B (2013) Reasoning about normative update. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI press, pp 20–26
Baines V, Padget J (2014) On the benefit of collective norms for autonomous vehicles. In: Proceedings of 8th international workshop on agents in traffic and transportation
Baines V, Padget J (2015) A situational awareness approach to intelligent vehicle agents. In: Modeling mobility with open data. Springer, Berlin, pp 77–103
Balke T, De Vos M, Padget J, Traskas D (2011) On-line reasoning for institutionally-situated bdi agents. In: The 10th international conference on autonomous agents and multiagent systems-volume 3, International foundation for autonomous agents and multiagent systems, pp 1109–1110
Baskar LD, De Schutter B, Hellendoorn J, Papp Z (2011) Traffic control and intelligent vehicle highway systems: a survey. IET Intell Transp Syst 5(1):38–52
Boella G, Van Der Torre L, Verhagen H (2006) Introduction to normative multiagent systems. Comput Math Organ Theory 12(2–3):71–79
Dastani M, Grossi D, Meyer J-JC, Tinnemeier N (2009) Normative multi-agent programs and their logics. In: Knowledge representation for agents and multi-agent systems. Springer, Berlin, pp 16–31
Hübner JF, Boissier O, Bordini RH (2011) A normative programming language for multi-agent organisations. Ann Math Artif Intell 62(1–2):27–53
Kavathekar P, Chen Y (2011) Vehicle platooning: a brief survey and categorization. In: ASME 2011 international design engineering technical conferences and computers and information in engineering conference. American society of mechanical engineers, pp 829–845
Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of sumo–simulation of urban mobility. Int J Adv Syst Meas 5(3–4)
Krauss S, Wagner P, Gawron C (1997) Metastable states in a microscopic model of traffic flow. Phys Rev E 55(5):5597
Markoff J (2015) Google cars drive themselves, in traffic. http://www.nytimes.com/2010/10/10/science/10google.html. Accessed 29 June 2015
Meneguzzi F, Vasconcelos W, Oren N, Luck M (2012) Nu-BDI: Norm-aware BDI agents. In: Proceedings of the 10th European workshop on multi-agent systems. Dublin, Ireland
Testerink B, Dastani M, Meyer J-J (2014) Norm monitoring through observation sharing. In: Proceedings of the European conference on social intelligence, pp 291–304
Testerink B, Dastani M, Meyer J-J (2014) Norms in distributed organizations. In: Coordination, organizations, institutions, and norms in agent systems IX. Springer, Berlin, pp 120–135
Tinnemeier NA, Dastani M, Meyer J-J, Torre L (2009) Programming normative artifacts with declarative obligations and prohibitions. In: Web intelligence and intelligent agent technologies. WI-IAT’09. IEEE/WIC/ACM international joint conferences on 2009, vol 2. IET, pp 145–152
van Riemsdijk MB, Hindriks K, Jonker C (2009) Programming organization-aware agents. In: Engineering societies in the agents world X. Springer, Berlin, pp 98–112
Wang Z, Kulik L, Ramamohanarao K (2007) Proactive traffic merging strategies for sensor-enabled cars. In: Proceedings of the fourth ACM international workshop on vehicular ad hoc networks. ACM, pp 39–48
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix 1: Merge Norm Scheme
For the merge norm scheme, we use the same pseudocode structure (Algorithm 2) as for the stay-on-lane norm scheme. As with the other norm scheme we begin with the instance of the norm (lines 0–8).7 Initially we read sensors 1 and 3 and merge the readings using the algorithm of Wang et al. [21] (line 1). The result is an ordered list of agents, which, if they continue as they are, will arrive at the merge point in the same order. We maintain a global variable \(t_{free}\) that indicates the next moment in time that the merge point is free. With optimalVelocity we calculate the optimal speed for an agent s.t. it will arrive at \(t_{free}\) on the merge point plus some safe margin, or later if the agent cannot make it in time physically (line 3). If the agent is at the right lane of the main road and the optimal velocity is below a predefined threshold, then it is obliged to move to the left lane (line 5), otherwise it is obliged to adapt its velocity to the optimal velocity and pass the merge point on the right lane (line 7). An agent is sanctioned if it is not passing the merge point on the correct lane (lines 11–12, and 15–16). Otherwise, an agent can also be sanctioned if it did not achieve its predetermined velocity (lines 19–20).
Appendix 2: Using Our Code
Our framework is open-source and available on-line on Github at https://github.com/baumfalk/TrafficMAS. It can be compiled from source, or it can be downloaded as a binary version.
1.1 How to Run It
Our framework can be run as follows. Assuming you use the binary JAR file, a scenario can be run with the following command:
java -jar TrafficMAS.jar ./scen/ scenario.mas.xml path/to/sumo scenario.sumocfg [seed].
In this command scen is the directory the scenario is located in, scenario.mas.xml is the main configuration file for the scenario and path/to/sumo denotes the SUMO executable to use. The SUMO-GUI program can also be used. The parameter scenario.sumocfg denotes the SUMO configuration file used by the scenario. Finally, the parameter seed is used to prepare the random number generator, which is used to spawn vehicles in a probabilistic fashion. If no seed is provided, a random one is generated by the system.
1.2 How to Create Your Own Scenario
Our framework also allows for the creation of your own scenarios. A TrafficMAS scenario consists of several XML files:
-
a global configuration file, containing the paths to the other XML files, as well as the simulation duration.
-
a configuration file specifying which norms are used. In this file, the norms are also parameterized with scenario-specific information, such as road names.
-
a configuration file which describes the norm-based traffic controllers. The file is used to define which controllers there are, which sensors they have access to and to which other controllers they are subscribed.
-
a configuration file containing the vehicle profile distributions. This file contains the distributions of the various driver profiles and the traffic density of the different roads.
-
various SUMO XML files: the XML file containing the nodes, the edges, the sensors and the routes.
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Baumfalk, J., Dastani, M., Poot, B., Testerink, B. (2019). A SUMO Extension for Norm-Based Traffic Control Systems. In: Behrisch, M., Weber, M. (eds) Simulating Urban Traffic Scenarios. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-33616-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-33616-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33614-5
Online ISBN: 978-3-319-33616-9
eBook Packages: EngineeringEngineering (R0)