Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches
This paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure within this paper does not encompass every type of f...
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IEEE
2022-01-01
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Online Access: | https://ieeexplore.ieee.org/document/9726148/ |
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author | Jongmin Lee Kwangseok Oh Sechan Oh Youngmin Yoon Sangyoon Kim Taejun Song Kyongsu Yi |
author_facet | Jongmin Lee Kwangseok Oh Sechan Oh Youngmin Yoon Sangyoon Kim Taejun Song Kyongsu Yi |
author_sort | Jongmin Lee |
collection | DOAJ |
description | This paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure within this paper does not encompass every type of failure and refers only to any situation where the upper controller or communications from the upper controller shuts down. When this type of failure occurs, the algorithm performs an emergency pullover maneuver. This maneuver does not require any form of independent control from the driver to be performed successfully. However, the highest control priority is still given to the driver if the driver intervenes during the maneuver. The feedback gain adaptation is comprised of two sections: Sensitivity Estimation and Gradient Descent (GD) based Adaptation. For Sensitivity Estimation, a relationship function has been designed with feedback gain, from the feedback gain adaptation, and changes in state error. The sensitivity of state error with respect to feedback gain can then be estimated. This estimation is done through the Recursive Least Squares (RLS) method with multiple forgetting factors through the directional forgetting method. For GD based Adaptation, state errors are applied with parameters for the cost-based learning to give Adaptation Gains. These Adaptation Gains are used in tandem with the estimated sensitivity to update the feedback gain. To reduce the number of tuning parameters required in the GD method, an additional distance condition has been proposed. This condition utilizes feedback change rates and state errors, obtained from the multi-dimensional plane of the feedback gain’s change rates. A proportional coefficient is also required as a tuning parameter for this condition. This parameter is tuned by a cost-based learning algorithm, also designed in this study. Resultantly, these methods allow the adaptive feedback controller to forgo any system information such as mathematical models and system parameters. This indicates that the vehicle model is not expected to hinder performance. Hence, controllers that do not require system information are indeed a preferable algorithm for fail-safe modules. Performance evaluations for the controller has also been conducted with actual vehicle tests, under longitudinal and lateral autonomous driving scenarios. |
first_indexed | 2024-12-11T14:54:59Z |
format | Article |
id | doaj.art-d932d0fda5d14f6eb4a9829d1faf446c |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-11T14:54:59Z |
publishDate | 2022-01-01 |
publisher | IEEE |
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series | IEEE Access |
spelling | doaj.art-d932d0fda5d14f6eb4a9829d1faf446c2022-12-22T01:01:20ZengIEEEIEEE Access2169-35362022-01-0110270142703010.1109/ACCESS.2022.31562759726148Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning ApproachesJongmin Lee0https://orcid.org/0000-0002-7777-1140Kwangseok Oh1https://orcid.org/0000-0003-2785-5298Sechan Oh2https://orcid.org/0000-0002-0349-2501Youngmin Yoon3https://orcid.org/0000-0001-5792-1271Sangyoon Kim4https://orcid.org/0000-0002-9210-563XTaejun Song5Kyongsu Yi6https://orcid.org/0000-0002-0484-9752Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaSchool of ICT, Robotics and Mechanical Engineering, Hankyong National University, Anseong-si, South KoreaSchool of ICT, Robotics and Mechanical Engineering, Hankyong National University, Anseong-si, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Research Engineering, Smart Mobility Lab, SML Company Ltd., Seoul, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaThis paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure within this paper does not encompass every type of failure and refers only to any situation where the upper controller or communications from the upper controller shuts down. When this type of failure occurs, the algorithm performs an emergency pullover maneuver. This maneuver does not require any form of independent control from the driver to be performed successfully. However, the highest control priority is still given to the driver if the driver intervenes during the maneuver. The feedback gain adaptation is comprised of two sections: Sensitivity Estimation and Gradient Descent (GD) based Adaptation. For Sensitivity Estimation, a relationship function has been designed with feedback gain, from the feedback gain adaptation, and changes in state error. The sensitivity of state error with respect to feedback gain can then be estimated. This estimation is done through the Recursive Least Squares (RLS) method with multiple forgetting factors through the directional forgetting method. For GD based Adaptation, state errors are applied with parameters for the cost-based learning to give Adaptation Gains. These Adaptation Gains are used in tandem with the estimated sensitivity to update the feedback gain. To reduce the number of tuning parameters required in the GD method, an additional distance condition has been proposed. This condition utilizes feedback change rates and state errors, obtained from the multi-dimensional plane of the feedback gain’s change rates. A proportional coefficient is also required as a tuning parameter for this condition. This parameter is tuned by a cost-based learning algorithm, also designed in this study. Resultantly, these methods allow the adaptive feedback controller to forgo any system information such as mathematical models and system parameters. This indicates that the vehicle model is not expected to hinder performance. Hence, controllers that do not require system information are indeed a preferable algorithm for fail-safe modules. Performance evaluations for the controller has also been conducted with actual vehicle tests, under longitudinal and lateral autonomous driving scenarios.https://ieeexplore.ieee.org/document/9726148/Automated vehiclefail-safeautonomous drivingsensitivity estimationcost-based learningrecursive least squares |
spellingShingle | Jongmin Lee Kwangseok Oh Sechan Oh Youngmin Yoon Sangyoon Kim Taejun Song Kyongsu Yi Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches IEEE Access Automated vehicle fail-safe autonomous driving sensitivity estimation cost-based learning recursive least squares |
title | Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches |
title_full | Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches |
title_fullStr | Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches |
title_full_unstemmed | Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches |
title_short | Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches |
title_sort | emergency pull over algorithm for level 4 autonomous vehicles based on model free adaptive feedback control with sensitivity and learning approaches |
topic | Automated vehicle fail-safe autonomous driving sensitivity estimation cost-based learning recursive least squares |
url | https://ieeexplore.ieee.org/document/9726148/ |
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