HEGEDÜS, Ferenc ;BÉCSI, Tamás ;ARADI, Szilárd ;GÁSPÁR, Péter . Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 65, n.3, p. 148-160, march 2019. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2018.5802.
Hegedüs, F., Bécsi, T., Aradi, S., & Gáspár, P. (2019). Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering, 65(3), 148-160. doi:http://dx.doi.org/10.5545/sv-jme.2018.5802
@article{sv-jmesv-jme.2018.5802,
author = {Ferenc Hegedüs and Tamás Bécsi and Szilárd Aradi and Péter Gáspár},
title = {Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {3},
year = {2019},
keywords = {automated driving; motion planning; trajectory planning, vehicle control; nonlinear optimization; artificial neural networks},
abstract = {Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods. This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs). First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle’s motion. Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning. The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method. The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time. Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted. Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated. Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions.},
issn = {0039-2480}, pages = {148-160}, doi = {10.5545/sv-jme.2018.5802},
url = {https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/}
}
Hegedüs, F.,Bécsi, T.,Aradi, S.,Gáspár, P. 2019 March 65. Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 65:3
%A Hegedüs, Ferenc %A Bécsi, Tamás %A Aradi, Szilárd %A Gáspár, Péter %D 2019 %T Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks %B 2019 %9 automated driving; motion planning; trajectory planning, vehicle control; nonlinear optimization; artificial neural networks %! Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks %K automated driving; motion planning; trajectory planning, vehicle control; nonlinear optimization; artificial neural networks %X Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods. This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs). First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle’s motion. Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning. The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method. The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time. Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted. Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated. Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions. %U https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/ %0 Journal Article %R 10.5545/sv-jme.2018.5802 %& 148 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 65 %N 3 %@ 0039-2480 %8 2019-03-27 %7 2019-03-27
Hegedüs, Ferenc, Tamás Bécsi, Szilárd Aradi, & Péter Gáspár. "Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks." Strojniški vestnik - Journal of Mechanical Engineering [Online], 65.3 (2019): 148-160. Web. 20 Dec. 2024
TY - JOUR AU - Hegedüs, Ferenc AU - Bécsi, Tamás AU - Aradi, Szilárd AU - Gáspár, Péter PY - 2019 TI - Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5802 KW - automated driving; motion planning; trajectory planning, vehicle control; nonlinear optimization; artificial neural networks N2 - Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods. This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs). First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle’s motion. Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning. The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method. The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time. Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted. Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated. Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions. UR - https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/
@article{{sv-jme}{sv-jme.2018.5802},
author = {Hegedüs, F., Bécsi, T., Aradi, S., Gáspár, P.},
title = {Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {3},
year = {2019},
doi = {10.5545/sv-jme.2018.5802},
url = {https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/}
}
TY - JOUR AU - Hegedüs, Ferenc AU - Bécsi, Tamás AU - Aradi, Szilárd AU - Gáspár, Péter PY - 2019/03/27 TI - Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 65, No 3 (2019): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5802 KW - automated driving, motion planning, trajectory planning, vehicle control, nonlinear optimization, artificial neural networks N2 - Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods. This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs). First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle’s motion. Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning. The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method. The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time. Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted. Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated. Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions. UR - https://www.sv-jme.eu/article/motion-planning-for-highly-automated-road-vehicles-with-a-hybrid-approach-using-nonlinear-optimization-and-artificial-neural-networks/
Hegedüs, Ferenc, Bécsi, Tamás, Aradi, Szilárd, AND Gáspár, Péter. "Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 65 Number 3 (27 March 2019)
Strojniški vestnik - Journal of Mechanical Engineering 65(2019)3, 148-160
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation. To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered. Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods. This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs). First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle’s motion. Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning. The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method. The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time. Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted. Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated. Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions.