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).