@inproceedings{ChaulwarBotschUtschick2016, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios}, booktitle = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5090-6167-9}, doi = {https://doi.org/10.1109/ICMLA.2016.0095}, pages = {540 -- 546}, year = {2016}, language = {en} } @inproceedings{ChaulwarBotschUtschick2017, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-4804-5}, doi = {https://doi.org/10.1109/IVS.2017.7995735}, pages = {297 -- 303}, year = {2017}, language = {en} } @inproceedings{ChaulwarBotschUtschick2018, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {Generation of Reference Trajectories for Safe Trajectory Planning}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4-7, 2018 : Proceedings, Part I}, editor = {Kurkov{\´a}, Vera and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-01418-6}, doi = {https://doi.org/10.1007/978-3-030-01418-6_42}, pages = {423 -- 434}, year = {2018}, language = {en} } @article{ChaulwarAlHashimiBotschetal.2021, author = {Chaulwar, Amit and Al-Hashimi, Hussein and Botsch, Michael and Utschick, Wolfgang}, title = {Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning}, volume = {11}, journal = {International Journal of Machine Learning and Computing}, number = {1}, publisher = {IACSIT Press}, address = {Singapur}, issn = {2010-3700}, doi = {https://doi.org/10.18178/ijmlc.2021.11.1.1007}, year = {2021}, 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.}, language = {en} }