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Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs

One of the most difficult challenges in providing a reliable and safe autonomous collision avoidance maneuver is developing the driver model that provides the planning system with the risk of an impending collision. Over the last few years, researchers have extensively studied several methodologies...

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Main Authors: Nurhaffizah, Hassan, Mohd Hatta, Mohammad Ariff, Hairi, Zamzuri, Sarah ‘Atifah, Saruchi, Nurbaiti, Wahid
Format: Book Chapter
Language:English
English
English
Published: Woodhead Publishing 2024
Subjects:
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/41786/1/Machine%20Intelligence%20in%20Mechanical%20Engineering.pdf
http://umpir.ump.edu.my/id/eprint/41786/2/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41786/3/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance.pdf
http://umpir.ump.edu.my/id/eprint/41786/
https://doi.org/10.1016/B978-0-443-18644-8.00017-4
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spelling my.ump.umpir.417862024-07-03T03:37:43Z http://umpir.ump.edu.my/id/eprint/41786/ Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs Nurhaffizah, Hassan Mohd Hatta, Mohammad Ariff Hairi, Zamzuri Sarah ‘Atifah, Saruchi Nurbaiti, Wahid TJ Mechanical engineering and machinery TS Manufactures One of the most difficult challenges in providing a reliable and safe autonomous collision avoidance maneuver is developing the driver model that provides the planning system with the risk of an impending collision. Over the last few years, researchers have extensively studied several methodologies to develop such a driver model. The human-like driver model, on the other hand, is a rarely discussed research topic. This work aims to develop a steering maneuver model in emergency collision avoidance that can imitate human emergency intervention. The neural network autoregressive with exogenous inputs (NNARX) is utilized to develop the model and autonomously predict the steering angle response. The work begins by collecting the avoidance maneuver driving data of the expert driver from the automaker company. In the data collection process, a controlled environment target scenario is used to ensure that all drivers encounter real emergencies. To investigate the performance of the developed model, a comparison prediction performance between the developed model and feed-forward neural network (FFNN) is presented. The finding shows that NNARX predicts the steering angle response with a lower prediction error during both training and testing compared to FFNN. Woodhead Publishing 2024-01 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41786/1/Machine%20Intelligence%20in%20Mechanical%20Engineering.pdf pdf en http://umpir.ump.edu.my/id/eprint/41786/2/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/41786/3/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance.pdf Nurhaffizah, Hassan and Mohd Hatta, Mohammad Ariff and Hairi, Zamzuri and Sarah ‘Atifah, Saruchi and Nurbaiti, Wahid (2024) Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs. In: Machine Intelligence in Mechanical Engineering. Woodhead Publishing Reviews: Mechanical Engineering Series . Woodhead Publishing, Sawston, United Kingdom, 359 -377. ISBN 978-0-443-18644-8 https://doi.org/10.1016/B978-0-443-18644-8.00017-4
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Nurhaffizah, Hassan
Mohd Hatta, Mohammad Ariff
Hairi, Zamzuri
Sarah ‘Atifah, Saruchi
Nurbaiti, Wahid
Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
description One of the most difficult challenges in providing a reliable and safe autonomous collision avoidance maneuver is developing the driver model that provides the planning system with the risk of an impending collision. Over the last few years, researchers have extensively studied several methodologies to develop such a driver model. The human-like driver model, on the other hand, is a rarely discussed research topic. This work aims to develop a steering maneuver model in emergency collision avoidance that can imitate human emergency intervention. The neural network autoregressive with exogenous inputs (NNARX) is utilized to develop the model and autonomously predict the steering angle response. The work begins by collecting the avoidance maneuver driving data of the expert driver from the automaker company. In the data collection process, a controlled environment target scenario is used to ensure that all drivers encounter real emergencies. To investigate the performance of the developed model, a comparison prediction performance between the developed model and feed-forward neural network (FFNN) is presented. The finding shows that NNARX predicts the steering angle response with a lower prediction error during both training and testing compared to FFNN.
format Book Chapter
author Nurhaffizah, Hassan
Mohd Hatta, Mohammad Ariff
Hairi, Zamzuri
Sarah ‘Atifah, Saruchi
Nurbaiti, Wahid
author_facet Nurhaffizah, Hassan
Mohd Hatta, Mohammad Ariff
Hairi, Zamzuri
Sarah ‘Atifah, Saruchi
Nurbaiti, Wahid
author_sort Nurhaffizah, Hassan
title Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
title_short Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
title_full Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
title_fullStr Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
title_full_unstemmed Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
title_sort human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
publisher Woodhead Publishing
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41786/1/Machine%20Intelligence%20in%20Mechanical%20Engineering.pdf
http://umpir.ump.edu.my/id/eprint/41786/2/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41786/3/Human-like%20driver%20model%20for%20emergency%20collision%20avoidance.pdf
http://umpir.ump.edu.my/id/eprint/41786/
https://doi.org/10.1016/B978-0-443-18644-8.00017-4
_version_ 1822924449454751744
score 13.235362

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