Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field
In order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehic...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/7504378 |
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| _version_ | 1832553214348623872 |
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| author | Ruibin Zhang Yingshi Guo |
| author_facet | Ruibin Zhang Yingshi Guo |
| author_sort | Ruibin Zhang |
| collection | DOAJ |
| description | In order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehicle and surrounding traffic participants is taken as input data for the Attention-Bidirectional Long-Short Term Memory (Attention-BiLSTM) model, which is trained to establish the desired mapping relationship. By predicting the motion state of the target vehicle and utilizing an improved risk field model based on the target vehicle of heading angle, the predictive risk field is obtained. This allows for the assessment of the ego vehicle operational risks. The risk warning model is integrated to provide risk early warning, and the safety path for the ego vehicle is planned based on the interaction between the predictive risk field equipotential lines and the cubic spline curves. Experimental results demonstrate that the proposed vehicle operation risk assessment and early warning model is effective in providing early warnings and safe path references for the ego vehicle in complex urban road test scenarios. |
| format | Article |
| id | doaj-art-a598acaa996041f1a2e5f932085dac2a |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a598acaa996041f1a2e5f932085dac2a2025-02-03T05:54:41ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/7504378Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk FieldRuibin Zhang0Yingshi Guo1School of Automobile EngineeringSchool of AutomobileIn order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehicle and surrounding traffic participants is taken as input data for the Attention-Bidirectional Long-Short Term Memory (Attention-BiLSTM) model, which is trained to establish the desired mapping relationship. By predicting the motion state of the target vehicle and utilizing an improved risk field model based on the target vehicle of heading angle, the predictive risk field is obtained. This allows for the assessment of the ego vehicle operational risks. The risk warning model is integrated to provide risk early warning, and the safety path for the ego vehicle is planned based on the interaction between the predictive risk field equipotential lines and the cubic spline curves. Experimental results demonstrate that the proposed vehicle operation risk assessment and early warning model is effective in providing early warnings and safe path references for the ego vehicle in complex urban road test scenarios.http://dx.doi.org/10.1155/2024/7504378 |
| spellingShingle | Ruibin Zhang Yingshi Guo Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field Journal of Advanced Transportation |
| title | Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field |
| title_full | Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field |
| title_fullStr | Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field |
| title_full_unstemmed | Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field |
| title_short | Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field |
| title_sort | research on intelligent vehicle operation risk assessment and early warning based on predictive risk field |
| url | http://dx.doi.org/10.1155/2024/7504378 |
| work_keys_str_mv | AT ruibinzhang researchonintelligentvehicleoperationriskassessmentandearlywarningbasedonpredictiveriskfield AT yingshiguo researchonintelligentvehicleoperationriskassessmentandearlywarningbasedonpredictiveriskfield |