Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields
Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety o...
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MDPI AG
2025-01-01
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| author | Yicheng Chen Dayi Qu Tao Wang Shanning Cui Dedong Shao |
| author_facet | Yicheng Chen Dayi Qu Tao Wang Shanning Cui Dedong Shao |
| author_sort | Yicheng Chen |
| collection | DOAJ |
| description | Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. |
| format | Article |
| id | doaj-art-2b4efe5a63844a8ca191d827861dfd77 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2b4efe5a63844a8ca191d827861dfd772025-08-20T02:12:37ZengMDPI AGApplied Sciences2076-34172025-01-01153130610.3390/app15031306Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential FieldsYicheng Chen0Dayi Qu1Tao Wang2Shanning Cui3Dedong Shao4College of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaCollege of Civil Engineering, Qingdao University of Technology, Qingdao 266520, ChinaConnected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design.https://www.mdpi.com/2076-3417/15/3/1306vehicle-to-vehicle interactionrisk quantificationmolecular potential fieldsnetworked autonomous vehiclesdeep learning |
| spellingShingle | Yicheng Chen Dayi Qu Tao Wang Shanning Cui Dedong Shao Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields Applied Sciences vehicle-to-vehicle interaction risk quantification molecular potential fields networked autonomous vehicles deep learning |
| title | Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields |
| title_full | Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields |
| title_fullStr | Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields |
| title_full_unstemmed | Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields |
| title_short | Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields |
| title_sort | quantification method of driving risks for networked autonomous vehicles based on molecular potential fields |
| topic | vehicle-to-vehicle interaction risk quantification molecular potential fields networked autonomous vehicles deep learning |
| url | https://www.mdpi.com/2076-3417/15/3/1306 |
| work_keys_str_mv | AT yichengchen quantificationmethodofdrivingrisksfornetworkedautonomousvehiclesbasedonmolecularpotentialfields AT dayiqu quantificationmethodofdrivingrisksfornetworkedautonomousvehiclesbasedonmolecularpotentialfields AT taowang quantificationmethodofdrivingrisksfornetworkedautonomousvehiclesbasedonmolecularpotentialfields AT shanningcui quantificationmethodofdrivingrisksfornetworkedautonomousvehiclesbasedonmolecularpotentialfields AT dedongshao quantificationmethodofdrivingrisksfornetworkedautonomousvehiclesbasedonmolecularpotentialfields |