Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach
Article Properties
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DOI (url)
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Publication Date2023/12/01
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Indian UGC (journal)
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Refrences40
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Citations4
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Xingyu Zhou University of Texas at Austin, Austin, TX, USA ORCID (unauthenticated)
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Heran Shen University of Texas at Austin, Austin, TX, USA ORCID (unauthenticated)
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Zejiang Wang University of Texas at Austin, Austin, TX, USA ORCID (unauthenticated)
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Hyunjin Ahn University of Texas at Austin, Austin, TX, USA ORCID (unauthenticated)
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Junmin Wang University of Texas at Austin, Austin, TX, USA ORCID (unauthenticated)
Cite
Zhou, Xingyu, et al. “Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach”. IEEE/ASME/Transactions/on/Mechatronics, vol. 28, no. 6, 2023, pp. 3017-28, https://doi.org/10.1109/tmech.2023.3236245.
Zhou, X., Shen, H., Wang, Z., Ahn, H., & Wang, J. (2023). Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach. IEEE/ASME/Transactions/on/Mechatronics, 28(6), 3017-3028. https://doi.org/10.1109/tmech.2023.3236245
Zhou, Xingyu, Heran Shen, Zejiang Wang, Hyunjin Ahn, and Junmin Wang. “Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach”. IEEE/ASME/Transactions/on/Mechatronics 28, no. 6 (2023): 3017-28. https://doi.org/10.1109/tmech.2023.3236245.
Zhou X, Shen H, Wang Z, Ahn H, Wang J. Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach. IEEE/ASME Transactions on Mechatronics. 2023;28(6):3017-28.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
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Electrical engineering
Electronics
Nuclear engineering
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Civil engineering (General)
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Technology
Mechanical engineering and machinery
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Modelling human control of steering for the design of advanced driver assistance systems | Annual Reviews in Control |
| 35 | 2017 |
A Two-Point Visual Control Model of Steering | Perception |
| 206 | 2004 |
Algebraic Driver Steering Model Parameter Identification | Journal of Dynamic Systems, Measurement and Control |
| 6 | 2022 |
| Shared Steering Control Between a Driver and an Automation: Stability in the Presence of Driver Behavior Uncertainty | IEEE Transactions on Intelligent Transportation Systems |
| 2 | 2013 |
| 10.1109/itsc.2013.6728261 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Authority Allocation Strategy for Shared Steering Control Considering Human-Machine Mutual Trust Level | IEEE Transactions on Intelligent Vehicles |
| 2 | 2024 |
| Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments | IEEE Transactions on Intelligent Vehicles |
| 2024 | |
| A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles | IEEE Internet of Things Journal | 2 | 2023 | |
| Fast Bidirectional Motion Planning for Self-Driving General N-Trailers Vehicle Maneuvering in Narrow Space | IEEE Open Journal of Intelligent Transportation Systems |
| 2023 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Fast Bidirectional Motion Planning for Self-Driving General N-Trailers Vehicle Maneuvering in Narrow Space and was published in 2023. The most recent citation comes from a 2024 study titled Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments. This article reached its peak citation in 2024, with 2 citations. It has been cited in 3 different journals, 33% of which are open access. Among related journals, the IEEE Transactions on Intelligent Vehicles cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.