Otonom Araçlar İçin Optimizasyon Tabanlı Yanal ve Doğrusal Yörünge Planlama

Yıl 2021, Sayı: 27, 539 - 548, 30.11.2021

İlhan Mutlu

https://doi.org/10.31590/ejosat.932390

Öz

Son yıllarda, teknolojik gelişmelerin de katkısıyla, otonom araçlar önemli bir ilgi odağı haline gelmiştir. Bu tarz karmaşık sistemlerde istenen performansın elde edilebilmesi için birçok alt problemin etkili bir şekilde çözülmüş olması gerekmektedir. Bu alt problemler detaylı olarak incelendiğinde hareket ve yörünge planlamasının çok önemli ve kritik bir yer tutuğu görülebilir. Bu çalışma kapsamında, yörünge planlama problemi Frenet koordinat düzleminde ele alınmış ve otonom sürüş sistemleri için optimizasyon tabanlı ve etkili bir yörünge planlama yaklaşımı önerilmiştir. Önerilen yöntem temelde bir optimizasyon probleminin analitik çözümünün çevrim dışı aşamada elde edilmesine dayanmaktadır. Böylece, gerçek zamanlı uygulama sırasında ilgili yörünge katsayılarının belirlenmesi için bir doğrusal denklem takımını çözmek yeterli hale gelmektedir. Karşılaşılan pratik sorunlar ve çözüm önerileri de yine bu çalışma kapsamında ele alınmıştır. Önerilen yöntemin etkinliği “Automotive Data and Time-Triggered Framework (ADTF)” ortamında gerçekleştirilen gerçek zamanlı benzetimlerle gösterilmiştir.

Anahtar Kelimeler

yol planlama, otonom araçlar, otonom sürüş, yörünge planlama

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Optimization Based Lateral and Longitudinal Trajectory Planning for Autonomous Vehicles

Öz

In recent years, autonomous vehicles have gained significant attention with the help of technological developments. To fulfill the desired performance expectations, various sub-problems have to be solved efficiently in such complex systems. When these sub-problems are examined in detail, it can be seen that the motion and trajectory planning play a crucial role. Within the scope of this study, the trajectory planning problem was handled in the Frenet coordinate frame and an efficient optimization-based trajectory planning approach was proposed for autonomous driving. The proposed method was essentially based on the analytical solution of an optimization problem which was derived in the offline phase. In the real-time application, only the corresponding trajectory coefficients should be determined by solving a linear set of equations. Encountered practical problems and solution recommendations were also discussed in this study. The effectiveness of the proposed method was demonstrated via real-time simulations that were realized in the “Automotive Data and Time-Triggered Framework (ADTF)” framework.

Anahtar Kelimeler

Autonomous vehicles, automated driving, trajectory planning, path planning

Proje Numarası

-

Kaynakça

• Açıkel, S., & Gökçen, A. (2019). Localization and point cloud based 3d mapping with autonomous robots. Avrupa Bilim ve Teknoloji Dergisi, 82-92.
• Eugensson, A., Brännström, M., Frasher, D., Rothoff, M., Solyom, S., & Robertsson, A. (2013, May). Environmental, safety legal and societal implications of autonomous driving systems. In International Technical Conference on the Enhanced Safety of Vehicles (ESV). Seoul, South Korea (Vol. 334).
• Gao, Y. (2014). Model predictive control for autonomous and semiautonomous vehicles (Doctoral dissertation, UC Berkeley).
• 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.
• González, D., Pérez, J., Milanés, V., & Nashashibi, F. (2015). A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1135-1145.
• Heinrich, S. (2018). Planning universal on-road driving strategies for automated vehicles (Vol. 119). Springer.
• Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?. Transportation Research Part A: Policy and Practice, 94, 182-193.
• Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., & How, J. P. (2009). Real-time motion planning with applications to autonomous urban driving. IEEE Transactions on control systems technology, 17(5), 1105-1118.
• Liberzon, D. (2011). Calculus of variations and optimal control theory: a concise introduction. Princeton university press.
• Mutlu, İ., Freese, M., Alaa, K., & Schrödel, F. (2019). Case Study on Model Free Determination of Optimal Trajectories in Highly Automated Driving. IFAC-PapersOnLine, 52(5), 205-211.
• Nicklas, M. (2013). Entwicklung eines Trajektorienplaners zum hochautomatischen Fahren im dynamischen Verkehrsumfeld. Technische Universitat Chemnitz, Fakultat für Mathematik.
• Schrödel, F., & Freese, M. (2019). Concept and Validation of a Guidance Approach for Highly Automated Shuttles. IFAC-PapersOnLine, 52(5), 359-365.
• Schrödel, F., & Schwarz, N. (2019). Case Study on a Proven Concept for Lateral Path Following Control. IFAC-PapersOnLine, 52(8), 344-349.
• Taş, Ö. Ş., Kuhnt, F., Zöllner, J. M., & Stiller, C. (2016, June). Functional system architectures towards fully automated driving. In 2016 IEEE Intelligent vehicles symposium (IV) (pp. 304-309). IEEE.
• Weiskircher, T., & Ayalew, B. (2015, July). Predictive adas: A predictive trajectory guidance scheme for advanced driver assistance in public traffic. In 2015 European Control Conference (ECC) (pp. 3402-3407). IEEE.
• Werling, M., Ziegler, J., Kammel, S., & Thrun, S. (2010, May). Optimal trajectory generation for dynamic street scenarios in a frenet frame. In 2010 IEEE International Conference on Robotics and Automation (pp. 987-993). IEEE.
• World Health Organization. (2015). Global status report on road safety 2015. World Health Organization.
• Voßwinkel, R., Mutlu, İ., Alaa, K., & Schrödel, F. (2020, May). A Modular and Model-Free Trajectory Planning Strategy for Automated Driving. In 2020 European Control Conference (ECC) (pp. 1186-1191). IEEE.
• Xu, W., Wei, J., Dolan, J. M., Zhao, H., & Zha, H. (2012, May). A real-time motion planner with trajectory optimization for autonomous vehicles. In 2012 IEEE International Conference on Robotics and Automation (pp. 2061-2067). IEEE.
• Yiğit, E., ONER, A. E., & Yöntem, O. (2020) Otonom Araçların Otomotiv Sektörüne Etkileri ve Beraberinde Getirdiği Yenilikler. Avrupa Bilim ve Teknoloji Dergisi, 181-186.
• Potzy, J., Goerigk, N., Heil, T., Fassbender, D., & Siedersberger, K. H. (2019). Trajectory Planning for Automated Merging on Highways. In VEHITS (pp. 283-290).
• Yang, D., Zheng, S., Wen, C., Jin, P. J., & Ran, B. (2018). A dynamic lane-changing trajectory planning model for automated vehicles. Transportation Research Part C: Emerging Technologies, 95, 228-247.
• Park, B., Lee, Y. C., & Han, W. Y. (2014). Trajectory generation method using Bézier spiral curves for high-speed on-road autonomous vehicles. In 2014 IEEE International Conference on Automation Science and Engineering (CASE) (pp. 927-932). IEEE.