doi: 10.17586/2226-1494-2019-19-1-155-165


OPTIMIZATION OF DANGEROUS SECTION PASSAGE FOR UNMANNED VEHICLES

I. A. Zikratov, I. I. Viksnin, T. V. Zikratova


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Zikratov I.A., Viksnin I.I., Zikratova T.V. Optimization of dangerous section passage for unmanned vehicles. Scientific and Technical Journal of Information Technologies, Mechanics and Optics , 2019, vol. 19, no. 1, pp. 155–165 (in Russian). doi: 10.17586/2226-1494-2019-19-1-155-165



Abstract
Subject of research. The research is focused on optimization management tasks of the ground unmanned vehicles in difficult conditions. We showed that it is possible to minimize passage time of road sections with narrowing and maintain security requirements in case of the centralized management. Method. A method of the unmanned cars movement is based on the dynamic programming model. The method provides faster movement than by full search of all possible variants for dangerous road section passage. Such result is achieved by the reorganization of the control task to the class of extreme tasks and the solution of such tasks is based on the Bellman recurrence equation. According to the proposed method a road section is considered as a system with discrete time and finite set of the end positions. Optimal plan of the cars movement is prepared by the central computing device, which has gathered information about road infrastructure and vehicles. The central device is sending control commands to the cars about the change of speed and movement direction in such a way that given criteria and restrictions are satisfied. Main results. The efficiency of the method is demonstrated by the example of driving on a two-lane road. One of the road lanes is blocked for traffic. It is proved that there is a solution that enables all cars to leave the dangerous road section in the shortest time with safety measures. Practical relevance. The proposed method is applicable for control of unmanned vehicles communicating with the central control device by wireless radio. The central control device is a part of the road infrastructure in conditions of isolated environment when there are no any fully autonomous vehicles in the group.

Keywords: unmanned vehicles, dynamic programming, traffic, Bellman equation

References
1. Carlino D., Boyles S.D., Stone P. Auction-based autonomous intersection management. Proc. 16th Int. IEEE Conf. on Intelligent Transportation Systems, ITSC 2013. Hague, Netherlands, 2013, pp. 529–534. doi: 10.1109/ITSC.2013.6728285
2. Wuthishuwong C., Traechtler A. Vehicle to infrastructure based safe trajectory planning for Autonomous Intersection Management. Proc. 13th Int. Conf. on ITS Telecommunications, ITST. Tampere, Finland, 2013, pp. 175–180. doi: 10.1109/ITST.2013.6685541
3. Vahidi A., Eskandarian A. Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation Systems, 2003, vol. 4, no. 3, pp. 143–153. doi: 10.1109/TITS.2003.821292
4. Ho C., Reed N., Spence C. Multisensory in-car warning signals for collision avoidance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2007, vol. 49, no. 6, pp. 1107–1114. doi: 10.1518/001872007X249965
5. Au T., Zhang S., Stone P. Autonomous intersection management for semi-autonomous vehicles. In Handbook of Transportation. Taylor & Francis, 2015, pp. 88–104.
6. Dresner K., Stone P. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 2008, vol. 31, pp. 591–656.
7. Wu J., Abbas-Turki A., El Moudni A. Cooperative driving: an ant colony system for autonomous intersection management. Applied Intelligence, 2012, vol. 37, no. 2, pp. 207–222. doi: 10.1007/s10489-011-0322-z
8. Zohdy I. H., Kamalanathsharma R. K., Rakha H. Intersection management for autonomous vehicles using iCACC. Proc. 15th Int. IEEE Conf. on Intelligent Transportation Systems, ITSC. Anchorage, USA, 2012, pp. 1109–1114. doi: 10.1109/ITSC.2012.6338827
9. Varaiya P. Smart cars on smart roads: problems of control. IEEE Transactions on Automatic Control, 1993, vol. 38, no. 2, pp. 195–207.Bazzan A.L.C. A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, 2005, vol. 10, no. 1, pp. 131–164. doi: 10.1007/s10458-004-6975-9
11. Beeson P. O'Quin J., Gillan B. et. al. Multiagent interactions in urban driving. Journal of Physical Agents, 2008, vol. 2, no. 1 pp. 15–29. doi: 10.14198/JoPha.2008.2.1.03
12. Halle S., Chaib-draa B. A collaborative driving system based on multiagent modelling and simulations. Transportation Research Part C: Emerging Technologies, 2005, vol. 13, no. 4, pp. 320–345. doi: 10.1016/j.trc.2005.07.004
13. Cremer M., Ludwig J. A fast simulation model for traffic flow on the basis of Boolean operations. Mathimatics and Computers Simulation, 1986, vol. 28, no. 4, pp. 297–303. doi: 10.1016/0378-4754(86)90051-0
14. Akhmadinurov M.M., Zavalishchin D.S., Timofeeva G.A. Mathematical Models of Traffic Management. Ekaterinburg UrSURT Publ., 2011, 120 p. (in Russian)
15. Alvarez I., Poznyak A., Malo A. Urban traffic control problem via a game theory application. Proc. 46th IEEE Conference on Decision and Control. New Orleans, 2007, pp. 2957–2961. doi: 10.1109/cdc.2007.4434820
16. Zikratov I.A., Viksnin I.I., Zikratova T.V. Multiagent planning of intersection passage by autonomous vehicles. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 5, pp. 839–849. doi: 10.17586/2226-1494-2016-16-5-839-849
17. Taha H.A. Operations Research: An Introduction. 8th ed. Prentice Hall, 2006, 838 p.


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