Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data
Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state forme...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/9/2846 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155296237813760 |
|---|---|
| author | Han Zhang Longfei Chen Bin Wang Xiaoyuan Wang Jingheng Wang Chenyang Jiao Kai Feng Cheng Shen Quanzheng Wang Junyan Han Yi Liu |
| author_facet | Han Zhang Longfei Chen Bin Wang Xiaoyuan Wang Jingheng Wang Chenyang Jiao Kai Feng Cheng Shen Quanzheng Wang Junyan Han Yi Liu |
| author_sort | Han Zhang |
| collection | DOAJ |
| description | Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. |
| format | Article |
| id | doaj-art-b0e3be68ae8c41dfbe2bb081995fc62e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b0e3be68ae8c41dfbe2bb081995fc62e2025-08-20T02:24:58ZengMDPI AGSensors1424-82202025-04-01259284610.3390/s25092846Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle DataHan Zhang0Longfei Chen1Bin Wang2Xiaoyuan Wang3Jingheng Wang4Chenyang Jiao5Kai Feng6Cheng Shen7Quanzheng Wang8Junyan Han9Yi Liu10College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaDepartment of Mathematics, Ohio State University, Columbus, OH 43220, USACollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, ChinaDriver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety.https://www.mdpi.com/1424-8220/25/9/2846road hypnosisdriverhuman–vehicleheterogeneous datafusion calculationvehicle |
| spellingShingle | Han Zhang Longfei Chen Bin Wang Xiaoyuan Wang Jingheng Wang Chenyang Jiao Kai Feng Cheng Shen Quanzheng Wang Junyan Han Yi Liu Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data Sensors road hypnosis driver human–vehicle heterogeneous data fusion calculation vehicle |
| title | Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data |
| title_full | Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data |
| title_fullStr | Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data |
| title_full_unstemmed | Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data |
| title_short | Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data |
| title_sort | research on the identification of road hypnosis based on the fusion calculation of dynamic human vehicle data |
| topic | road hypnosis driver human–vehicle heterogeneous data fusion calculation vehicle |
| url | https://www.mdpi.com/1424-8220/25/9/2846 |
| work_keys_str_mv | AT hanzhang researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT longfeichen researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT binwang researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT xiaoyuanwang researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT jinghengwang researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT chenyangjiao researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT kaifeng researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT chengshen researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT quanzhengwang researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT junyanhan researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata AT yiliu researchontheidentificationofroadhypnosisbasedonthefusioncalculationofdynamichumanvehicledata |