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Intelligent Parallel Parking Using Adaptive Neuro-Fuzzy Inference System Based on Fuzzy C-Means Clustering Algorithm
Technical Paper
2018-01-5029
ISSN: 0148-7191, e-ISSN: 2688-3627
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Automotive Technical Papers
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English
Abstract
Today’s intelligent self-driving vehicles alongside technology development are more believable. One of the intelligent features of self-driving cars is autonomous parking which has been specifically considered in industry and academic research areas. This paper focuses on the autonomous parallel parking. First, the vehicle kinematics modeling by considering Ackermann angle calculation has been thoroughly explained and then the desired path by satisfying spatial conditions and its proportional steering angle is extracted. Autonomous parking scenario has been defined based on two phases of forward and backward motions. Accordingly, the desired training data includes steering angle and vehicle motion feedbacks (x, y, φ) that are utilized for designing intelligent controller. The proposed control system has two levels: upper and lower level. The former is a supervisory controller which switches between phases while the latter controls the vehicle based on received feedbacks from sensors in each phase. In this research adaptive-network-based fuzzy inference system (ANFIS) based on fuzzy c-means clustering (FCM) is employed to model the expert driver as an intelligent controller in parking maneuver. In this structure, FCM is used to systematically create the fuzzy membership functions and rule base for ANFIS. The performance of the proposed control algorithm is verified by defining an accuracy index. The simulation results in three different constant speeds indicate the value of accuracy index and jerk of controller output signals remains in an acceptable band.
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Rezaei Nedamani, H., Masnadi Khiabani, P., and Azadi, S., "Intelligent Parallel Parking Using Adaptive Neuro-Fuzzy Inference System Based on Fuzzy C-Means Clustering Algorithm," SAE Technical Paper 2018-01-5029, 2018, https://doi.org/10.4271/2018-01-5029.Data Sets - Support Documents
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References
- Azadi , S.H. , RezaeiNedamani , H.R. , and Kazemi , R. Automatic Parking of an Articulated Vehicle Using ANFIS Global Journal of Science, Engineering and Technology 14 93 104 2013 2322-2441
- Pérez-Morales , D. , Domínguez-Quijada , S. , Kermorgant , O. , and Martinet , P. Autonomous Parking Using a Sensor Based Approach International Conference on Intelligent Transportation Systems 2016
- Liu , W. , Li , Z. , Li , L. , and Wang , F.-Y. Parking Like a Human: A Direct Trajectory Planning Solution IEEE Transactions on Intelligent Transportation Systems 18 12 3388 3397 2017
- Jeong , Y. , Kim , S. , Yi , K. , Lee , S. et al. Design and Implementation of Parking Control Algorithm for Autonomous Valet Parking SAE Technical Paper 2016-01-0146 2016 10.4271/2016-01-0146
- Paul , A. , Chauhan , R. , Srivastava , R. , and Baruah , M. Advanced Driver Assistance Systems SAE Technical Paper 2016-28-0223 2016 10.4271/2016-28-0223
- Yan , W. , Deng , J. , and Dezhi , X. Data-Driven Automatic Parking Constrained Control for Four-Wheeled Mobile Vehicles International Journal of Advanced Robotic Systems 13 6 2016 10.1177/1729881416663667
- Filatov , D.M. and Serykh , E.V. Intelligence Autonomous Parking Control System of Four-Wheeled Vehicle 2016 XIX IEEE International Conference on Soft Computing and Measurements (SCM) 2016 154 156
- Yang , W. , Zheng , L. , Li , Y. , Ren , Y. et al. A Trajectory Planning and Fuzzy Control for Autonomous Intelligent Parking System SAE Technical Paper 2017-01-0032 2017 10.4271/2017-01-0032
- Li , B. , Wang , K. , and Shao , Z. Time-Optimal Maneuver Planning in Automatic Parallel Parking Using a Simultaneous Dynamic Optimization Approach IEEE Transactions on Intelligent Transportation Systems 17 11 3263 3274 2016
- Li , B. , Zhang , Y. , and Shao , Z. Spatio-Temporal Decomposition: A Knowledge-Based Initialization Strategy for Parallel Parking Motion Optimization Knowledge-Based Systems 107 179 196 2016
- Patten , W.N. , Wu , H.-C. , and Cai , W. Perfect Parallel Parking via Pontryagin’s Principle Journal of Dynamic Systems, Measurement, and Control 116 4 723 728 1994
- Zhao , J.-S. , Liu , X. , Feng , Z.-J. , and Dai , J.S. Design of an Ackermann-Type Steering Mechanism Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227 11 2549 2562 2013
- Kosko , B. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence/Book and Disk 1 Prentice Hall 1992
- Nauck , D. , Klawonn , F. , and Kruse , R. Foundations of Neuro-Fuzzy Systems John Wiley & Sons, Inc. 1997
- Jang , J.-S.R. ANFIS: Adaptive-Network-Based Fuzzy Inference System IEEE Transactions on Systems, Man, and Cybernetics 23 3 665 685 1993
- Gao , X. and Xie , W. Advances in Theory and Applications of Fuzzy Clustering Chinese Science Bulletin 45 11 961 970 2000
- Bezdek , J.C. , Ehrlich , R. , and Full , W. FCM: The Fuzzy c-Means Clustering Algorithm Computers & Geosciences 10 2-3 191 203 1984
- Karahoca , A. and Karahoca , D. GSM Churn Management by Using Fuzzy C-Means Clustering and Adaptive Neuro Fuzzy Inference System Expert Systems with Applications 38 3 1814 1822 2011
- Park , S.-H. , Kim , S.-J. , Lim , K.-J. , and Kang , S.-H. Comparison of Recognition Rates between BP and ANFIS with FCM Clustering Method on Off-Line PD Diagnosis of Defect Models of Traction Motor Stator Coil Proceedings of 2005 International Symposium on Electrical Insulating Materials, 2005 (ISEIM 2005) 3 2005 849 852