What is social coordination in AVs?

As the research and studies are going on, grouping and classifying the social status of how to deal with AVs are also playing a major contribution.

Social Coordination- It has been defined as a conflict involving the social dilemmas between the self-interest of the agent’s short-term and longer-term of the group’s collective interest. In driving, the occurrence of social dilemmas is present since the coordination needs to be done between the drivers for the safety and efficient joint maneuvers. Some examples include resource depletion, low voter turnout, overpopulation, the prisoner’s dilemma, or the game by public goods. The consideration of traffic merges or opening and closing a gap often helps us to predict human behavior so that better decision-making is possible to improve the group efficiency.

In my opinion, one of crucial factor in designing new concepts related to vehicle technology or transportation is keep in mind about the social dilemmas. As each country or continent got their own set of rules such as following right hand or left-hand rule then it is necessary to keep in mind how the navigation system can handle the heavy traffic scenario. The predicting behaviour of the traffic merge is also important for a better decision-making capability.

Source:- S. Ross, G. Gordon, D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, G. Gordon, D. Dunson, M. Dud´ık, Eds. (Proceedings of Machine Learning Research, Fort Lauderdale, FL, 2011), vol. 15, pp. 627–635;

J. Ho, S. Ermon, Generative adversarial imitation learning in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett, Eds. (Neural Information Processing Systems Foundation, 2016), pp. 4565–4573;

B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey, “Maximum entropy inverse reinforcement learning” in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, A. Cohn, Ed. (Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 2008), vol. 8, pp. 1433–1438;

H. Kretzschmar, M. Spies, C. Sprunk, W. Burgard, Socially compliant mobile robot navigation via inverse reinforcement learning. Int. J. Robot. Res. 35, 1289–1307 (2016);

D. Sadigh, S. Sastry, S. A. Seshia, A. D. Dragan, “Planning for autonomous cars that leverage effects on human actions” in Proceedings of Robotics: Science and Systems, D. Hsu, N. Amato, S. Berman, S. Jacobs, Eds. http://www.roboticsproceedings.org/rss12/p29.html. Accessed 15 November 2019.

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