Motion Planning for Autonomous Vehicles

Year 2018, Volume: 1 Issue: 2, 20 - 29, 31.12.2018

Ahmet Kemal Nennioğlu , Tahsin Köroğlu

Abstract

In recent
years, autonomous vehicles which are capable of travelling without human
intervention are the last point reached in the light of intelligent systems and
can perform almost all tasks successfully in controlled environments. Road and
traffic situation in autonomous vehicles are modelled with the help of
communication systems and sensors, then the motion of the vehicle is designed
by implementing various techniques and algorithms. Being the link
between perception and control stages, motion planning has become an
interesting research field. In this field, trajectory planning, which fulfills
the task of determining the route to be followed by the vehicle, is the topic
in which a great progress has been made.In this paper, the descriptions and
applications of the motion planning techniques and algorithms are presented in
detail by classifying according to the literature. The current status, the
gaps, the needs and future focus of the technology are pointed out by examining
previous studies. In addition to these, it is emphasized that further
researches on the subject of maneuverability in uncertain and dynamic
environments should be carried out by considering the issues such as safety,
comfort and energy saving.

Keywords

Intelligent transportation systems, Automotive engineering, Autonomous vehicles

Otonom araçlarda hareket planlaması

Abstract

Son yıllarda,
otomotiv teknolojisinde insan müdahalesi olmadan yolculuk gerçekleştirebilen
otonom araçlar, akıllı sistemler ışığında gelinen son nokta olup kontrollü
çevrelerde neredeyse tüm görevleri başarıyla yerine getirebilmektedir. Otonom
araçlarda mevcut yol ve trafik durumu iletişim sistemleri ve sensörler
aracılığıyla modellenmekte ve çeşitli tekniklerle aracın hareketi tasarlanmaktadır.
Algılama ve kontrol aşamaları arasında bağlantı kuran hareket planlaması, son
yıllarda üzerine çalışılan önemli bir araştırma alanı olmuştur. Bu alanda,
taşıtın izleyeceği yolu belirleme görevini yerine getiren yörünge planlaması
büyük aşama kaydedilen konulardan biri olarak dikkat çekmektedir. Bu makalede, literatürde
yer alan hareket planlama teknikleri ve algoritmaları sınıflandırılarak
tanımlamaları ve uygulama örnekleri detaylı olarak sunulmuştur. Literatürde yer
alan çalışmalar incelenerek teknolojinin bugünkü durumu, boşlukları,
ihtiyaçları ve gelecekteki odak noktaları belirtilmiştir. Bununla birlikte güvenlik,
konfor ve enerji tasarrufu gibi konular göz önünde bulundurularak, hareketli ve
belirsiz çevrelerdeki aracın manevra kabiliyeti konusunda da araştırmalar
yapılmasına ihtiyaç duyulduğu vurgulanmıştır.

Keywords

Akıllı ulaşım sistemleri, Otomotiv mühendisliği, Otonom araçlar

References

• Gökaşar, I., & Dündar, S. Sürücüsüz taşıtların trafik akım hızına etkisinin yapay sinir ağları ile incelenmesi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 1(2), 59-75.
• González, D., Pérez, J., Milanés, V., & Nashashibi, F. (2016). A Review of Motion Planning Techniques for Automated Vehicles. IEEE Trans. Intelligent Transportation Systems, 17(4), 1135-1145.
• SAE, T. (2016). Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. SAE Standard J3016.
• Matzka, S., & Altendorfer, R. (2009). A comparison of track-to-track fusion algorithms for automotive sensor fusion Multisensor Fusion and Integration for Intelligent Systems (pp. 69-81): Springer.
• Aufrère, R., Gowdy, J., Mertz, C., Thorpe, C., Wang, C.-C., & Yata, T. (2003). Perception for collision avoidance and autonomous driving. Mechatronics, 13(10), 1149-1161.
• Lozano-Perez, T., Mason, M. T., & Taylor, R. H. (1984). Automatic synthesis of fine-motion strategies for robots. The International Journal of Robotics Research, 3(1), 3-24.
• Nagel, R., Eichler, S., & Eberspacher, J. (2007). Intelligent wireless communication for future autonomous and cognitive automobiles. Intelligent Vehicles Symposium, IEEE.
• Hwang, Y. K., & Ahuja, N. (1992). Gross motion planning—a survey. ACM Computing Surveys (CSUR), 24(3), 219-291.
• Jo, K., Kim, J., Kim, D., Jang, C., & Sunwoo, M. (2014). Development of autonomous car—Part I: Distributed system architecture and development process. IEEE Transactions on Industrial Electronics, 61(12), 7131-7140.
• Jo, K., Kim, J., Kim, D., Jang, C., & Sunwoo, M. (2015). Development of autonomous car—Part II: A case study on the implementation of an autonomous driving system based on distributed architecture. IEEE Transactions on Industrial Electronics, 62(8), 5119-5132.
• Tounsi, M., & Le Corre, J. (1996). Trajectory generation for mobile robots. Mathematics and computers in simulation, 41(3-4), 367-376.
• Chung, S.-Y., & Huang, H.-P. (2011). Robot motion planning in dynamic uncertain environments. Advanced Robotics, 25(6-7), 849-870.
• Du Toit, N. E., & Burdick, J. W. (2012). Robot motion planning in dynamic, uncertain environments. IEEE Transactions on Robotics, 28(1), 101-115.
• Shladover, S. E., Desoer, C. A., Hedrick, J. K., Tomizuka, M., Walrand, J., Zhang, W.-B., McKeown, N. (1991). Automated vehicle control developments in the PATH program. IEEE Transactions on vehicular technology, 40(1), 114-130.
• Behringer, R., & Muller, N. (1998). Autonomous road vehicle guidance from autobahnen to narrow curves. IEEE Transactions on Robotics and Automation, 14(5), 810-815.
• Noto, M., & Sato, H. (2000). A method for the shortest path search by extended Dijkstra algorithm.IEEE International Conference on Systems, Man, and Cybernetics, 2000.
• Anderson, S. J., Karumanchi, S. B., & Iagnemma, K. (2012). Constraint-based planning and control for safe, semi-autonomous operation of vehicles. Intelligent Vehicles Symposium (IV), IEEE.
• Bacha, A., Bauman, C., Faruque, R., Fleming, M., Terwelp, C., Reinholtz, C.,Anderson, D. (2008). Odin: Team victortango's entry in the darpa urban challenge. Journal of Field Robotics, 25(8), 467-492.
• Bohren, J., Foote, T., Keller, J., Kushleyev, A., Lee, D., Stewart, A., . . . Satterfield, B. (2008). Little ben: The ben franklin racing team's entry in the 2007 DARPA urban challenge. Journal of Field Robotics, 25(9), 598-614.
• Likhachev, M., & Ferguson, D. (2009). Planning long dynamically feasible maneuvers for autonomous vehicles. The International Journal of Robotics Research, 28(8), 933-945.
• Ziegler, J., Werling, M., & Schroder, J. (2008). Navigating car-like robots in unstructured environments using an obstacle sensitive cost function. Paper presented at the 2008 IEEE Intelligent Vehicles Symposium.
• Ferguson, D., Stentz, A., & Thrun, S. (2004). PAO for planning with hidden state.IEEE International Conference on Robotics and Automation, ICRA'04.
• Pivtoraiko, M., & Kelly, A. (2005). Efficient constrained path planning via search in state lattices. International Symposium on Artificial Intelligence, Robotics, and Automation in Space.
• Howard, T. M., Green, C. J., Kelly, A., & Ferguson, D. (2008). State space sampling of feasible motions for high‐performance mobile robot navigation in complex environments. Journal of Field Robotics, 25(6‐7), 325-345.
• Kushleyev, A., & Likhachev, M. (2009). Time-bounded lattice for efficient planning in dynamic environments.IEEE International Conference on Robotics and Automation, ICRA'09..
• Li, Q., Zeng, Z., Yang, B., & Zhang, T. (2009). Hierarchical route planning based on taxi GPS-trajectories.17th International Conference on Geoinformatics..
• Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Huhnke, B. (2008). Junior: The stanford entry in the urban challenge. Journal of Field Robotics, 25(9), 569-597.
• Ziegler, J., & Stiller, C. (2009). Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios.IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2009..
• Elbanhawi, M., & Simic, M. (2014). Sampling-based robot motion planning: A review. IEEEAccess, 2, 56-77.
• Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30(7), 846-894.
• Ferguson, D., Kalra, N., & Stentz, A. (2006). Replanning with rrts.IEEE International Conference on Robotics and Automation, ICRA 2006.
• Brezak, M., & Petrović, I. (2014). Real-time approximation of clothoids with bounded error for path planning applications. IEEE Transactions on Robotics, 30(2), 507-515.
• Reeds, J., & Shepp, L. (1990). Optimal paths for a car that goes both forwards and backwards. Pacific journal of mathematics, 145(2), 367-393.
• Funke, J., Theodosis, P., Hindiyeh, R., Stanek, G., Kritatakirana, K., Gerdes, C., Huhnke, B. (2012). Up to the limits: Autonomous Audi TTS.IEEE Intelligent Vehicles Symposium (IV),.
• Xu, W., Wei, J., Dolan, J. M., Zhao, H., & Zha, H. (2012). A real-time motion planner with trajectory optimization for autonomous vehicles.IEEE International Conference on Robotics and Automation (ICRA).
• Bautista, D. G., Rastelli, J. P., Lattarulo, R., Milanés, V., & Nashashibi, F. (2014). Continuous curvature planning with obstacle avoidance capabilities in urban scenarios. IEEE 17th International Conference on Intelligent Transportation Systems (ITSC).
• Glaser, S., Vanholme, B., Mammar, S., Gruyer, D., & Nouveliere, L. (2010). Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Transactions on Intelligent Transportation Systems, 11(3), 589-606.
• Petrov, P., & Nashashibi, F. (2014). Modeling and Nonlinear Adaptive Control for Autonomous Vehicle Overtaking. IEEE Trans. Intelligent Transportation Systems, 15(4), 1643-1656.
• Wang, L., Miura, K. T., Nakamae, E., Yamamoto, T., & Wang, T. J. (2001). An approximation approach of the clothoid curve defined in the interval [0, π/2] and its offset by free-form curves. Computer-Aided Design, 33(14), 1049-1058.