An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme
Purpose – Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-11-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JICV-06-2019-0007/full/pdf?title=an-adaptive-model-predictive-approach-for-automated-vehicle-control-in-fallback-procedure-based-on-virtual-vehicle-scheme |
| _version_ | 1797333608402255872 |
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| author | Wei Xue Rencheng Zheng Bo Yang Zheng Wang Tsutomu Kaizuka Kimihiko Nakano |
| author_facet | Wei Xue Rencheng Zheng Bo Yang Zheng Wang Tsutomu Kaizuka Kimihiko Nakano |
| author_sort | Wei Xue |
| collection | DOAJ |
| description | Purpose – Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction. Design/methodology/approach – Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes. Findings – It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios. Originality/value – This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped. |
| first_indexed | 2024-03-08T08:07:24Z |
| format | Article |
| id | doaj.art-4ce2119925224ed6882c07827cdda843 |
| institution | Directory Open Access Journal |
| issn | 2399-9802 |
| language | English |
| last_indexed | 2024-03-08T08:07:24Z |
| publishDate | 2020-11-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj.art-4ce2119925224ed6882c07827cdda8432024-02-02T10:15:19ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022020-11-0122677710.1108/JICV-06-2019-0007637238An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle schemeWei Xue0Rencheng Zheng1Bo Yang2Zheng Wang3Tsutomu Kaizuka4Kimihiko Nakano5Institute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, Japan and Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, JapanPurpose – Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction. Design/methodology/approach – Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes. Findings – It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios. Originality/value – This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.https://www.emerald.com/insight/content/doi/10.1108/JICV-06-2019-0007/full/pdf?title=an-adaptive-model-predictive-approach-for-automated-vehicle-control-in-fallback-procedure-based-on-virtual-vehicle-schememodel predictive controlautomated vehiclesfallbacksensor failurevirtual vehicle scheme |
| spellingShingle | Wei Xue Rencheng Zheng Bo Yang Zheng Wang Tsutomu Kaizuka Kimihiko Nakano An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme Journal of Intelligent and Connected Vehicles model predictive control automated vehicles fallback sensor failure virtual vehicle scheme |
| title | An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| title_full | An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| title_fullStr | An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| title_full_unstemmed | An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| title_short | An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| title_sort | adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme |
| topic | model predictive control automated vehicles fallback sensor failure virtual vehicle scheme |
| url | https://www.emerald.com/insight/content/doi/10.1108/JICV-06-2019-0007/full/pdf?title=an-adaptive-model-predictive-approach-for-automated-vehicle-control-in-fallback-procedure-based-on-virtual-vehicle-scheme |
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