Detection of Driving Capability Degradation for Human-Machine Cooperative Driving
Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detecti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/7/1968 |
| _version_ | 1797571812255596544 |
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| author | Feng Gao Bo He Yingdong He |
| author_facet | Feng Gao Bo He Yingdong He |
| author_sort | Feng Gao |
| collection | DOAJ |
| description | Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition. |
| first_indexed | 2024-03-10T20:45:57Z |
| format | Article |
| id | doaj.art-6c07ba5cd2074b89a548fdef88689053 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:45:57Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6c07ba5cd2074b89a548fdef886890532023-11-19T20:20:35ZengMDPI AGSensors1424-82202020-04-01207196810.3390/s20071968Detection of Driving Capability Degradation for Human-Machine Cooperative DrivingFeng Gao0Bo He1Yingdong He2School of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaDepartment of Intelligent Vehicle, Chang’an Global Automobile Research Center, Chongqing 401133, ChinaMechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USADue to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.https://www.mdpi.com/1424-8220/20/7/1968automatic drivingcooperative drivingdriving capabilitydriving riskdriver statedriver model |
| spellingShingle | Feng Gao Bo He Yingdong He Detection of Driving Capability Degradation for Human-Machine Cooperative Driving Sensors automatic driving cooperative driving driving capability driving risk driver state driver model |
| title | Detection of Driving Capability Degradation for Human-Machine Cooperative Driving |
| title_full | Detection of Driving Capability Degradation for Human-Machine Cooperative Driving |
| title_fullStr | Detection of Driving Capability Degradation for Human-Machine Cooperative Driving |
| title_full_unstemmed | Detection of Driving Capability Degradation for Human-Machine Cooperative Driving |
| title_short | Detection of Driving Capability Degradation for Human-Machine Cooperative Driving |
| title_sort | detection of driving capability degradation for human machine cooperative driving |
| topic | automatic driving cooperative driving driving capability driving risk driver state driver model |
| url | https://www.mdpi.com/1424-8220/20/7/1968 |
| work_keys_str_mv | AT fenggao detectionofdrivingcapabilitydegradationforhumanmachinecooperativedriving AT bohe detectionofdrivingcapabilitydegradationforhumanmachinecooperativedriving AT yingdonghe detectionofdrivingcapabilitydegradationforhumanmachinecooperativedriving |