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IJMLC 2021 Vol.11(1): 1-11 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1007

Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning

Amit Chaulwar, Hussein Al-Hashimi, Michael Botsch, and Wolfgang Utschick

Abstract—The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.

Index Terms—Safe trajectory planning, hybrid machine learning, collision avoidance and mitigation.

Faculty of Electrical Enginering, Ingolstadt University of Applied Sciences, Ingolstadt, Germany (e-mail: firstname.lastname@thi.de).
Wolfgang Utschick is with the Department of Electrical Engineering, Technical University of Munich, Munich, Germany (e-mail: utschick@tum.de).


Cite: Amit Chaulwar, Hussein Al-Hashimi, Michael Botsch, and Wolfgang Utschick, "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 1-11, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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