Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our wo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6649 |
| _version_ | 1797597838318764032 |
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| author | Timotej Gruden Grega Jakus |
| author_facet | Timotej Gruden Grega Jakus |
| author_sort | Timotej Gruden |
| collection | DOAJ |
| description | In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs. |
| first_indexed | 2024-03-11T03:11:07Z |
| format | Article |
| id | doaj.art-02abe0a6c5a74642a780deb889fc62a0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:07Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-02abe0a6c5a74642a780deb889fc62a02023-11-18T07:34:53ZengMDPI AGApplied Sciences2076-34172023-05-011311664910.3390/app13116649Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated DrivingTimotej Gruden0Grega Jakus1Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaIn conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.https://www.mdpi.com/2076-3417/13/11/6649automated vehiclesconditionally automated drivingtake-overdriving parametersdata mining |
| spellingShingle | Timotej Gruden Grega Jakus Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving Applied Sciences automated vehicles conditionally automated driving take-over driving parameters data mining |
| title | Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving |
| title_full | Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving |
| title_fullStr | Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving |
| title_full_unstemmed | Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving |
| title_short | Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving |
| title_sort | determining key parameters with data assisted analysis of conditionally automated driving |
| topic | automated vehicles conditionally automated driving take-over driving parameters data mining |
| url | https://www.mdpi.com/2076-3417/13/11/6649 |
| work_keys_str_mv | AT timotejgruden determiningkeyparameterswithdataassistedanalysisofconditionallyautomateddriving AT gregajakus determiningkeyparameterswithdataassistedanalysisofconditionallyautomateddriving |