Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/4938 |
| _version_ | 1797532399001665536 |
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| author | Jude Chibuike Nwadiuto Hiroyuki Okuda Tatsuya Suzuki |
| author_facet | Jude Chibuike Nwadiuto Hiroyuki Okuda Tatsuya Suzuki |
| author_sort | Jude Chibuike Nwadiuto |
| collection | DOAJ |
| description | This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model. |
| first_indexed | 2024-03-10T10:59:31Z |
| format | Article |
| id | doaj.art-776d1fee5f3442abae592ff6d97eb902 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:59:31Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-776d1fee5f3442abae592ff6d97eb9022023-11-21T21:38:46ZengMDPI AGApplied Sciences2076-34172021-05-011111493810.3390/app11114938Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX ModelJude Chibuike Nwadiuto0Hiroyuki Okuda1Tatsuya Suzuki2Department of Mechanical Systems Engineering Nagoya University, Furocho Chikusa Ward, Nagoya 464-8601, JapanDepartment of Mechanical Systems Engineering Nagoya University, Furocho Chikusa Ward, Nagoya 464-8601, JapanDepartment of Mechanical Systems Engineering Nagoya University, Furocho Chikusa Ward, Nagoya 464-8601, JapanThis paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.https://www.mdpi.com/2076-3417/11/11/4938driving behaviordriver modelhybrid system identificationstatistical variable selectionconsistent variable selectionPWARX model |
| spellingShingle | Jude Chibuike Nwadiuto Hiroyuki Okuda Tatsuya Suzuki Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model Applied Sciences driving behavior driver model hybrid system identification statistical variable selection consistent variable selection PWARX model |
| title | Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model |
| title_full | Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model |
| title_fullStr | Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model |
| title_full_unstemmed | Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model |
| title_short | Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model |
| title_sort | driving behavior modeling based on consistent variable selection in a pwarx model |
| topic | driving behavior driver model hybrid system identification statistical variable selection consistent variable selection PWARX model |
| url | https://www.mdpi.com/2076-3417/11/11/4938 |
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