A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking

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A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle-dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique-parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers.


  • [1] K. Min, J. Choi, H. Kim, and H. Myung, Design and implementation of path generation algorithm for controlling autonomous driving and parking, in 12th International Conference on Control, Automation and Systems (ICCAS), Jeju Island, Korea, 2012, pp. 956-959

  • [2] I. E. Paromtchik and C. Laugier, Autonomous parallel parking of a nonholonomic vehicle, in Proc. of the IEEE Intelligent Vehicles Symposium, Tokyo, Japan, 1996, pp. 13-18

  • [3] J. Z. Kolter, C. Plagemann, D. T. Jackson, A. Y. Ng, and S. Thrun, A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving, in IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA, 2010, pp. 839-845

  • [4] T. K. Lau, Learning autonomous drift parking from one demonstration, in Proc. of the IEEE International Conference on Robotics and Biomimetics, Phuket, Thailand, 2011, pp. 1456-1461

  • [5] M. R. Heinen, F. S. Osório, F. J. Heinen, and C. Kelber, Seva3d: Using artificial neural networks to autonomous vehicle parking control, in 2006 International Joint Conference on Neural Networks, Vancouver, BC, Canada, 2006, pp. 4704-4711

  • [6] N. N. Samani, J. Ghaisari, and M. Danesh, 2nd international econference on computer and knowledge engineering (iccke), in 2006 International Joint Conference on Neural Networks, 2012, pp. 117-122

  • [7] D. Gorinevsky, A. Kapitanovsky, and A. Goldenberg, Design of radial basis function-based controller for autonomous parking of wheeled vehicles, in Proc. of the American Control Conference, Baltimore, Maryland, 1994, pp. 806-810

  • [8] S. Kim, W. Liu, and K. A. Marczuk, Autonomous parking from a random drop point, in 2014 IEEE Intelligent Vehicles Symposium (IV), Dearborn, Michigan, USA, 2014, pp. 498-503

  • [9] Y. Wang and X. Zhu, Hybrid fuzzy logic controller for optimized autonomous parking, in American Control Conference (ACC), Washington, DC, USA, 2013, pp. 182-187

  • [10] Z. Joung, K. J. W. X. DongJi, and K. Y. Bae, A study of autonomous parking for a 4-wheel driven mobile robot, in Proc. of the 26th Chinese Control Conference, Zhangjiajie, Hunan, China, 2007, pp. 179-184

  • [11] R. Schubert, E. Richter, and G. Wanielik, Comparison and evaluation of advanced motion models for vehicle tracking, in 11th International Conference on Information Fusion, 2008

  • [12] B. Siciliano and O. Kathib, Springer Handbook of Robotics, Berlin, Germany: Springer, 2008

  • [13] P. S. Maybeck, Stochastic Models, Estimation and Control, New York, NY, USA: Academic Press, 1979, vol. 1

  • [14] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer-Verlag, 2001

  • [15] P. Banerjee and R. Nevatia, Dynamics based trajectory segmentation for uav videos, in Conference on Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International, Boston, MA: IEEE, 2010, pp. 345 - 352

  • [16] J. H. Friedman, On bias, variance, 0/1-loss and the curse of dimensionality, Data Mining and Knowledge Discovery, vol. 1, pp. 55-77, 1997

  • [17] T. G. Dietterich, Machine learning research: Four current directions, AI Magazine, vol. 18, no. 4, pp. 97-136, 1997

  • [18] L. Breiman, Random forests, Machine Learning, vol. 45, no. 1, pp. 5-32, 2001

  • [19] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, ser. The Wadsworth & Brooks/Cole Statistics/Probability Series, Wadsworth, 1984

  • [20] M. Botsch and J. A. Nossek, Construction of interpretable radial basis function classifiers based on the random forest kernel, in IEEE World Congress on Computational Intelligence 2008 (WCCI 2008), 2008

  • [21] L. Breiman, Out-of-bag estimation, University of California, Berkeley, Tech. Rep., 1996

  • [22] J. Schürmann, Pattern Classification: a unified view of statistical and neural approaches, John Wiley & Sons, 1996

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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