Comparing Collision Avoidance Systems of Different Type of Transportation Mode

Year 2016, Volume: 2 Issue: 1, 37 - 48, 01.11.2016

Serkan Özdemir Remzi Fışkın Hakkı Kişi

Abstract

Different modes of transportation are often used in our daily lives. Therefore, how safe these modes are commonly researched by researchers. Many models and methods are developed to avoidcollision with the development of technology. This development is aimed to improving the safety of life and property. The technological developments also aim to reduce the minimum level of the humanerror. Technological devices developed to prevent collision are applied in systematic way according to type of transportation mode. When comparatively examined, it is similar to each other technology used indifferent modes. In this respect, proposed model and methods are similar in general. These approaches are generally based on position of vehicles relative to each other and also rules have been developed taking into consideration the possibilities that may occur. Real-time sensors used toavoid collision in vehicles reduce risk of collision and provide significantachievements on behalf of avoiding collision. Besides this, it has beenconsidered important a communication network between vehicles. As a result, the importance of the technological devices developed to ensure collision avoidance is increasing in our life. Thus, the study aims to explain and compare the methods, models and techniques used in the different transportation modes so as to avoid collision.Keywords: Collision avoidance, Transportation mode, Autonomoussystems, Artificial intelligence

Keywords

Transportation systems, Artificial intelligence Collision mode, avoidance, Autonomous

Farklı Taşıma Modlarının Çatışmadan Kaçınma Sistemlerinin Karşılaştırılması

Abstract

Günlük hayatımızda farklı ulaşım modları sıklıkla kullanılmaktadır. Bu sebeple bu modların ne kadar güvenli olduğu sıklıkla araştırılmaktadır. Teknolojinin gelişmesi ile birlikte çatışmadan kaçınma amacıyla birçok model ve metot geliştirilmektedir. Bu gelişmeler can ve mal güvenliği arttırmayı amaçlamaktadır. Teknolojik uygulamaların amacı insan hatasını en az seviyeye indirmektir. Çatışmayı önlemek için geliştirilen teknolojik donanımlar önem sırasına göre farklı ulaşım yollarında sistematik bir şekilde uygulanmaktadır. Karşılaştırmalı olarak incelendiğinde ise farklı ulaşım modlarında kullanılan teknolojiler birbirlerine benzemektedir. Bu bakımdan, önerilen model ve metotlar birbirine benzer niteliktedir. Genel olarak bu hesaplamalarda ulaşım araçlarının birbirlerine göre konumları baz alınmış, oluşabilecek ihtimaller göz önüne alınarak kurallar geliştirilmiştir. Ulaşım araçlarında çatışmayı önlemek için kullanılan gerçek zamanlı sensörler riski azaltarak, çatışmadan kaçınma adına önemli ölçüde başarılar sağlamaktadır. Bunun yanında araçlar arası bir iletişim ağı ile doğrudan haberleşmeye de önem verilmiştir. Sonuç olarak çatışmadan kaçınmayı sağlamak için geliştirilen teknolojik donanımların hayatımızdaki önemi gün geçtikçe artmaktadır. Buradan hareketle, bu araştırmada günümüz teknolojileri ile farklı ulaşım modlarındaki çatışmadan kaçınma amacıyla kullanılan metotlar, teknikler ve yöntemlerin açıklanması ve karşılaştırılması hedeflenmektedir

Keywords

Çatışmadan kaçınma, Ulaşım modları, Otonom sistemler, Yapay zeka

References

• Vahidi, A., Eskandarian, A., (2003). Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation Systems 4(3): 143-153.
• Jansson, J., Ekmark, J., Gustafsson, F., 2002. Decision making for collision avoidance systems. In: SAE technical paper 2002-01-0403, Society of Automotive Engineers 2002 World Congress, Vol. SP-1662, Detroit, MI, USA.
• Tamura, M., Inoue, H., Watanabe, T., Maruko, N., 2001. Research on a brake assist system with a preview function. In: SAE technical paper 2001- 01-0357, Society of Automotive Engineers 2001 World Congress, Detroit, MI, USA.
• Jansson, J., Gustafsson, F., (2008). A Framework and automotive applications of collision avoidance decision making, Automatica 44(9): 2347-2351.
• Jansson, J., (2005). Collision avoidance theory with
• mitigation, Department of Electrical Engineering, Linköping University, Linköping, Sweden.
• Li, L. N., Yang, S. H., Cao, B. G., Li, Z. F., (2006). A summary of studies on the automation of ship collision avoidance intelligence, Journal of Jimei University 11(2): 188-192.
• Tsou, M. C., Hsueh, C. K., (2010). The study of ship collision avoidance route planning by ant colony algorithm, Journal of Marine Science and Technology 18(5): 746-756.
• Szłapczyński, R., (2007). Determining the optimal course alteration manoeuvre in a multi-target encounter situation for a given ship domain model, Annual of Navigation 12: 75-85.
• Perera, L. P., Soares, C. G., (2015). Collision risk detection and quantification in ship navigation with
• Engineering 109: 344-354. systems,
• Ocean Chang, K. Y., Jan, G., (2003). A method for searching optimal routes with collision avoidance on raster charts, The Journal of Navigation 56: 371-384.
• Zhang, J., Zhang, D., Yan, X., Haugen, S., Soares, C. G., (2015). A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs, Ocean Engineering 105: 336-348.
• Itoh, H., Numano, M., Pedersen, E., (2003). Modelling and simulation of sea traffic and a visualization-based collision avoidance support system. Papers of National Maritime Research Institute 3(5).
• Lazarowska, A., (2012). Decision support system for collision avoidance at sea, Polish Maritime Research 19(74): 19-24.
• Zhu, X., Xu, H., Lin, J., (2001). Domain and its model based on neural networks, Journal of Navigation 54(1): 97-103.
• Zeng, X., (2003). Evolution of the safe path for ship navigation, Applied Artificial Intelligence 17(2): 87-104.
• Smierzchalski, R., Michalewicz, Z., (1998). Modeling of ship trajectory in collision situations by an evolutionary algorithm, IEEE Transactions on Evolutionary Computation 20:1-18.
• Hwang, C. N., (2002). The integrated design of fuzzy collision-avoidance and autopilots on ships, The Journal of Navigation 55: 117-136.
• Harris, C. J., Hong, X., Wilson, P. A., (1999). An intelligent guidance and control system for ship obstacle avoidance, Proceedings of the Institution of Mechanical Engineers, Part 1: Journal of Systems and Control Engineering 213: 311-320.
• Chohra, A., Farah, A., Belloucif, M., (1997). Neuro-fuzzy expert system E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles, Advanced Robotics 12(6): 629-649.
• Borenstein, J., Koren, Y., (1989). Real-time obstacle avoidance for fast mobile robots, IEEE Transactions on Systems, Man, and Cybernetics 19(5): 1179-1187.
• Lee, Y. I., Kim, S. G., Kim, Y. G., (2015). Fuzzy relational product for collision avoidance of autonomous ships, Intelligent Automation and Soft Computing 21(1): 21-38.
• Zadeh, L. A., (1965). Fuzzy Sets. Information and Control 8: 338–353.
• Tam, C. K., Bucknall, R., Greig, A., (2009). Review of collision avoidance and path planning methods for ships in close range encounters, The Journal of Navigation 62: 455-476.
• Kuchar, J. K., Drumm, A. C., (2007). The traffic alert and collision avoidance system, Lincoln Laboratory Journal 16(2): 277-296.
• Williamson, T., Spencer, N. A., (1989). Development and operation of the traffic alert and collision avoidance system (TCAS). Proceedings of the IEEE, 77(11): 1735-1744.
• Kochenderfer, M. J., Chryssanthacopoulos, J. P., Weibel, R. E., 2011. A new approach for designing safer collision avoidance systems, Ninth USA / Europe Air Traffic Management Research and Development Seminar, Massachusetts Institute of Technology Lexington, Massachusetts, USA.
• Park, P., Tomlin, C., 2012. Investigating communication infrastructure of next generation air traffic management, IEEE/ACM Third International Conference on Cyber-Physical Systems, 35-44.
• Ferrara, A., Paderno, J., (2006). Application of switching control for automatic pre-crash collision avoidance in cars, Nonlinear Dyn. 46: 307-321.
• Ramesh, S., Ranjan, R., Mukherjee, R., Chaudhuri, S., (2012). Vehicle collision avoidance system using wireless sensor networks, International Journal of Soft Computing and Engineering 2(5): 300-303.
• Mahmud, S. M., Shanker, S., (2003). An architecture for intelligent automotive collision avoidance systems. Ionosphere 5: 183-188.
• Saijyothsna, T., Umamaheswari, P., (2014). Collision avoidance of trains by creating mutual communication
• International Journal of Innovative Research in Computer and Communication Engineering 2(7): 5203-5208. embedded
• system, Strang, T., Lehner, A., Rico Garcia, C., Heirich, O., Grosch, A., 2011. Cooperative situation awareness for a railway collision avoidance system (RCAS). In Adjunct Proceedings.