DERİN ÖĞRENME TABANLI VE PID KONTROL TABANLI SÜRÜCÜSÜZ ARAÇ SİSTEMLERİ

Yıl 2020, Cilt: 8 Sayı: 5, 306 - 316, 29.12.2020

Koray Aki , Ahmet Emir Dirik

https://doi.org/10.21923/jesd.829598

Cited By: 1

Öz

İnsan müdahalesi olmadan kendi kendine hareket edebilen araçlar sürücüsüz araç olarak adlandırılmaktadır. Sürücüsüz araçlar son yirmi yılda; askeri, lojistik ve endüstriyel üretimdeki potansiyel uygulamaları ile hem akademiden, hem de endüstriden büyük ilgi görmeye başlamıştır. Sürücüsüz araçların kullanılması günümüz trafiğinin çevresel etkilerini azaltmak ve trafik kazalarını önlemek gibi birçok konuda toplumsal fayda sağlamaktadır. Sürücüsüz araçlarda navigasyon için GPS, çarpışmaları önlemek için sensör ve nesneleri tespit etmek için kamera gibi çeşitli teknolojiler kullanılmaktadır. Bu teknolojilerin hepsi ya da birkaçı kullanılarak Derin Öğrenme tabanlı ve PID kontrol ile otonom sürüş yapılabilmektedir. Bu çalışmada Derin Öğrenme Tabanlı model eğitimi ve otonom sürüş testleri sürüş simülatöründe gerçekleştirilmiştir. Sürüş simülatöründen aracın direksiyon açısı, hız bilgisi ve ön camına monte edilen üç kameradan (sağ, sol ve orta) görüntü bilgisi alınmıştır. Aracın otonom hareketi Derin Öğrenme tabanlı model eğitimi gerçekleştirilerek ve PID kontrol ile sağlanmıştır. Bu çalışmada Derin Öğrenme ile eğitilen modelin sürüş performansı ile PID kontrol ile gerçekleştirilen sürüş performansı sürüş simülatöründe bir tam turda karşılaştırılmıştır. Aracın sürüş parkurundaki bir tam turda gerçek zamanlı olarak özerk hareketi kaydedilmiş ve başarım değerlendirmesi gerçekleştirilmiştir. Sürüş simülatöründe gerçekleştirilen testler sonucunda PID kontrol tabanlı sürüşte de başarılı sonuçlar elde edilmiş olmasına rağmen, Derin Öğrenme tabanlı modelin performansının daha iyi olduğu belirlenmiştir.

Anahtar Kelimeler

derin öğrenme, sürücüsüz araç, CNN, PID, evrişimsel sinir ağları

DEEP LEARNING BASED AND PID CONTROL BASED AUTONOMOUS VEHICLE SYSTEMS

Öz

Vehicles that can move on their own without human intervention are called autonomous vehicles. Over the last two decades, autonomous vehicles have been receiving considerable interest from both academia and industry, with potential applications in military, logistics and industrial production. The development of autonomous vehicles provides social benefits in many aspects, such as reducing the number of deaths and reducing the environmental impact of today's traffic. Autonomous vehicles use various technologies such as GPS for navigation, sensors to avoid collisions, and cameras for object detection. Autonomous driving can be performed with Deep Learning and PID control. In this study, Deep Learning Based model training and autonomous driving tests were carried out in the driving simulator. Steering angle, speed information from the driving simulator and image information from three cameras (right, left and middle) mounted on the windshield were obtained. Autonomous movement of the vehicle was provided by performing Deep Learning based model training and PID control. In this study, the driving performance of the model trained with Deep Learning and the driving performance performed by PID control were compared in one full tour in the driving simulator. Autonomous movement of the vehicle was recorded in real time during one full lap on the driving track and performance evaluation was carried out. As a result of the tests carried out in the driving simulator, although successful results were obtained in PID control-based driving, it was determined that the performance of the Deep Learning based model was better.

Anahtar Kelimeler

deep learning, CNN, PID, autonomous vehicle, convolution Neural Networks

Kaynakça

• Aki., K. 2019a. Sürüş Simülatöründe Derin Öğrenme Tabanlı Sürücüsüz Araç Testi https://www.youtube.com/watch?v=01kLVx6xMQQ-(Erişim tarihi: 07.02.2020).
• Aki., K. 2019b. Sürüş Simülatöründe PID Tabanlı Sürücüsüz Araç Testi, https://www.youtube.com/watch?v=QtRFsFQRv3g-(Erişim tarihi: 01.02.2020).
• Anonim, 2019. PID Kontrol, https://en.wikipedia.org/wiki/PID_controller-(Erişim tarihi: 01.02.2020).
• Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L., Monfort, M., Muller, U., Zhang, J. 2016. End to End Learning for Self-Driving Cars. https:// arxiv.org/pdf/1604.07316.pdf-(Erişim tarihi: 06.02.2020).
• Bojarski, M., Yeres, P., Choromanska, A., Choromanski, K., Firner, B., Jackel, L., Muller, U. 2017. Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car. https://arxiv.org/pdf/1704.07911.pdf-(Erişim tarihi: 06.02.2020).
• Broggi, A., Cerri, P., Felisa, M., Laghi, M. C., Mazzei, L., Porta, P. P. 2012. The Vislab Intercontinental Autonomous Challenge: An Extensive Test for a Platoon of Intelligent Vehicles. International Journal of vehicle Autonomous System, 10: 147-164.
• Broogi, A., Cerri, P., Debattisti, S., Laghi, M. C., Medici, P., Molinari, D., Panciroli, M., Prioletti, A. 2015. Proud-Public Road Urban Driverless-Car Test. Intelligent Transportation Systems, 16(6): 3508-3519.
• Cerri, P., Soprani, G., Zani, P., Choi, J., Lee, J., Kim, D., Yi, K., Broggi, A. 2011. Computer Vision at the Hyundai Autonomous Challenge. International IEEE Conference on Intelligent Transportation Systems (ITSC), 5-7 Oct. 2011, Washington, DC, USA.
• Chandni, C.K., Sajith Variyar, V.V., Guruvayurappan, K. 2017. Vision Based Closed Loop PID Controller Design and Implementation for Autonomous Car. International Conference on Advances in Computing, Communications and Informatics (ICACCI). 13-16 September 2017, Udupi, India.
• Cho, H., Seo, Y., Vijaya Kumar, B.V.K., Rajkumar, R.R. 2014. A Multi-Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving Environments. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), 31 May–7 June 2014, Hong Kong, China.
• Chollet, F. 2018. Deep Learning with Python, Ed: Arritola, T., Gaines, J., Taylor, T., NY, USA, pp: 4-23.
• Chowdhary, P., Gupta, V., Gupta, D., Jadhav, A., Mishra, V. 2018. Design of Two Wheel Self Balancing Robot Using PID Controller. International Journal of Engineering Research & Technology, 5(1): 1-3
• Copot, D., Ghita, M., Ionescu, C. M. 2019. Simple Alternatives to PID-Type Control for Processes with Variable Time-Delay. Processes, 7(3): 1-16.
• Dickmanns, E.D., Zapp, A. 1987. Autonomous High Speed Road Vehicle Guidance by Computer Vision. IFAC Proceedings Volumes, 20(5): 221–226.
• Emirler, M. T., Uygan, İ. M. C., Güvenç, B. A., Güvenç, L. 2014. Robust PID Steering Control in Parameter Space for Highly Automated Driving. International Journal of Vehicular Technology, 2014(3): 1-8.
• Gurghian, A., Koduri, T., Bailur, S. V., Carey, K. J., Murali, V. N. 2016. DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 June-1 July 2016, Las Vegas, NV, USA.
• Han, G., Fu, W., Wang, W., Wu, Z. 2017. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network, Journal of Sensors, 17(6): 1244.
• Hinton, G. E., Salakhutdinov, R. R., 2006. Reducing the Dimensionality of Data with Neural Networks, Computer Science. New Life for Neural Networks, 313: 504-507.
• Jigang, H., Jie, W., Hui, F. 2017. An Anti-Windup Self-Tuning Fuzzy PID Controller for Speed Control of Brushless DC Motor. Journal for Control, Measurement, Electronics, Computing and Communications, 58(3): 321-335.
• Jiao, J., Chen, W. W., Shao-Wen, L.I., Wang, J. X. 2008. Self-adaptive PID Control for Intelligent Vehicle Steering System Based on IPSO. Journal of Anhui University, 29:1325–1327.
• Kanade, T., Thorpe, C., Whittaker, W. 1986. Autonomous land vehicle project at CMU. Proceedings of the 1986 ACM Fourteenth Annual Conference on Computer Science; 4–6 February 1986, OH, USA.
• Khare, Y. B., Yaduvir, S. 2010. PID Control of Heat Exchanger System. International Journal of Computer Applications, 8(6): 22-27.
• Lie, G., Zejian, R., Pingshu, Ge., Jing, C. 2014. Advanced Emergency Braking Controller Design for Pedestrian Protection Oriented Automotive Collision Avoidance System. The Scientific World Journal, 2014: 1-11.
• Montemerlo, M., Thrun, S., Dahlkamp ,H., Stavens, D., Strohband, S. 2006. Winning the DARPA Grand Challenge with an AI Robot. Proceedings of the 21st National Conference on Artificial Intelligence, 16–20 July 2006, MA, USA.
• Nie, L., Guan, J., Lu, C., Zheng, H., Yin, Z. 2018. Longitudinal Speed Control of Autonomous Vehicle Based on a Self-Adaptive PID of Radial Basis Function Neural Network. IET Intelligent Transport Systems, 12(6): 485-494.
• Omijeh, B. O., Ehikhamenle, M., Promise, E. 2015. Simulated Design of Water Level Control System. Computer Engineering and Intelligent Systems. 6(1): 30-40.
• Ravankar, A., Ravankar, A.A., Kobayashi, Y., Hoshino, Y., Peng, C.-C. Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges. Journal of Sensors, 18(9): 3170.
• Surendharan, S., Jennifer Ranjani, J. 2016. Environment Conscious Automated Vehicle Navigation System using PID Controller. Indian Journal of Science and Technology, 9(48): 1-5.
• Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G. 2006. Stanley: The Robot That Won the DARPA Grand Challenge. Journal of Field Robot, 23(9): 661–692.
• Velazquez, M., Cruz, D., Garcia, S. 2016. Velocity and Motion Control of a Self-Balancing Vehicle Based on a Cascade Control Strategy. International Journal of Advanced Robotic Systems, 13(3): 1-11.
• Wallace, R., Stentz, A., Thorpe, C., Moravec, H., Whittaker, W., Kanade, T. 1985. First results in robot road-following, Proceedings of the 9th International Joint Conference on Artificial Intelligence, 18–23 August 1985, CA, USA.
• Xin, J., Wang, C., Zhang, Z., Zheng, N. 2014. China Future Challenge: Beyond the Intelligent Vehicle, IEEE Intelligent Transportation Systems. 16( 2): 8-10.
• Yang, Q., Li, G., Kang, X. 2008. Application of Fuzzy PID Control in The Heating System. 2008 Chinese Control and Decision Conference, 2-4 July 2008, Yantai, Shandong, China.
• Zhao, P., Chen, Y., Song, Y., Tao, X., Xu, T., Mei, T. 2012. Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID. International Journal of Advanced Robotic Systems, 9(44): 1-11.
• Zhou, X., Gao, H., Jia, Y., Li, L., Zhao, L., Yu R. 2019. Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications. Journal of Sensors, 2019(2):1-15.
• Ziegler, J. ve ark. 2014. Making Bertha Drive an Autonomous Journey on a Historic Route. Intelligent Transportation Systems Magazine, 6(2): 8-20.
• Zimit, A. Y., Yap, H. J., Hamza, M. F. Siradjuddin, I. 2018. Modelling and Experimental Analysis Two-Wheeled Self Balance Robot Using PID Controller. International Conference on Computational Science and Its Applications, 2-5 July 2018, Melbourne, VIC, Australia.