TY - CHAP A1 - Chaulwar, Amit A1 - Al-Hashimi, Hussein A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Efficient hybrid machine learning algorithm for trajectory planning in critical traffic-scenarios T2 - The 4th International Conference on Intelligent Transportation Engineering, ICITE 2019 UR - https://doi.org/10.1109/ICITE.2019.8880266 KW - Trajectory Planning KW - Hybrid Machine Learning KW - Embedded Implementation Y1 - 2019 UR - https://doi.org/10.1109/ICITE.2019.8880266 SN - 978-1-7281-4553-2 SP - 196 EP - 202 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Chaulwar, Amit A1 - Al-Hashimi, Hussein A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning JF - International Journal of Machine Learning and Computing N2 - 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. UR - https://doi.org/10.18178/ijmlc.2021.11.1.1007 KW - Safe trajectory planning KW - hybrid machine learning KW - collision avoidance and mitigation Y1 - 2021 UR - https://doi.org/10.18178/ijmlc.2021.11.1.1007 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14099 SN - 2010-3700 VL - 11 IS - 1 PB - IACSIT Press CY - Singapur ER -