Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling
IntroductionEnsuring safe driving and effective risk control is critical for the dynamic operation of autonomous new energy vehicles (NEVs), particularly under complex traffic conditions. A key challenge lies in unifying risk assessment across diverse driving scenarios, which hinders reliable and ad...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Mechanical Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2025.1658915/full |
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| author | Huilan Li Huilan Li Yun Wan Junning Shang Xiangyang Xu |
| author_facet | Huilan Li Huilan Li Yun Wan Junning Shang Xiangyang Xu |
| author_sort | Huilan Li |
| collection | DOAJ |
| description | IntroductionEnsuring safe driving and effective risk control is critical for the dynamic operation of autonomous new energy vehicles (NEVs), particularly under complex traffic conditions. A key challenge lies in unifying risk assessment across diverse driving scenarios, which hinders reliable and adaptive risk management in autonomous systems.MethodsTo address this challenge, a local traffic risk evaluation framework tailored for NEVs is proposed, grounded in the principle of least action. The method constructs a virtual force system centered on the autonomous NEV, integrating three components: (1) virtual risk force to capture vehicle-to-vehicle interaction risks; (2) virtual driving force reflecting the vehicle’s motion intention; and (3) virtual regulatory force to enforce traffic rule compliance. By modeling the action of this force system, a novel metric for local traffic safety is formulated, enabling real-time risk assessment and informing control strategies.ResultsThe proposed method was validated through simulations across typical hazardous scenarios, including rear-end collisions, emergency deceleration, lane changes, and intersection conflicts. Simulations demonstrated that the framework enables timely risk perception and adaptive control behaviors (e.g., braking, evasive lane changes), which substantially improved the driving safety of NEVs.DiscussionThis work provides a unified and computationally efficient tool for enhancing risk-aware decision-making and control in autonomous NEVs. By addressing the challenge of scenario-unified risk assessment, it contributes to the safer deployment of autonomous NEVs and their more intelligent integration into traffic systems. |
| format | Article |
| id | doaj-art-d8f597dbfe7248aa88ec7cd09de1b5ce |
| institution | Kabale University |
| issn | 2297-3079 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj-art-d8f597dbfe7248aa88ec7cd09de1b5ce2025-08-20T03:39:09ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792025-08-011110.3389/fmech.2025.16589151658915Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modelingHuilan Li0Huilan Li1Yun Wan2Junning Shang3Xiangyang Xu4College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaCollege of New Energy Vehicle Engineering, Chongqing City Vocational College, Chongqing, ChinaCollege of New Energy Vehicle Engineering, Chongqing City Vocational College, Chongqing, ChinaCollege of New Energy Vehicle Engineering, Chongqing City Vocational College, Chongqing, ChinaCollege of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaIntroductionEnsuring safe driving and effective risk control is critical for the dynamic operation of autonomous new energy vehicles (NEVs), particularly under complex traffic conditions. A key challenge lies in unifying risk assessment across diverse driving scenarios, which hinders reliable and adaptive risk management in autonomous systems.MethodsTo address this challenge, a local traffic risk evaluation framework tailored for NEVs is proposed, grounded in the principle of least action. The method constructs a virtual force system centered on the autonomous NEV, integrating three components: (1) virtual risk force to capture vehicle-to-vehicle interaction risks; (2) virtual driving force reflecting the vehicle’s motion intention; and (3) virtual regulatory force to enforce traffic rule compliance. By modeling the action of this force system, a novel metric for local traffic safety is formulated, enabling real-time risk assessment and informing control strategies.ResultsThe proposed method was validated through simulations across typical hazardous scenarios, including rear-end collisions, emergency deceleration, lane changes, and intersection conflicts. Simulations demonstrated that the framework enables timely risk perception and adaptive control behaviors (e.g., braking, evasive lane changes), which substantially improved the driving safety of NEVs.DiscussionThis work provides a unified and computationally efficient tool for enhancing risk-aware decision-making and control in autonomous NEVs. By addressing the challenge of scenario-unified risk assessment, it contributes to the safer deployment of autonomous NEVs and their more intelligent integration into traffic systems.https://www.frontiersin.org/articles/10.3389/fmech.2025.1658915/fulldriving riskrisk assessmentvirtual force modelingautonomous drivingnew energy vehicles |
| spellingShingle | Huilan Li Huilan Li Yun Wan Junning Shang Xiangyang Xu Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling Frontiers in Mechanical Engineering driving risk risk assessment virtual force modeling autonomous driving new energy vehicles |
| title | Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| title_full | Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| title_fullStr | Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| title_full_unstemmed | Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| title_short | Risk-aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| title_sort | risk aware local traffic safety evaluation for autonomous new energy vehicles based on virtual force modeling |
| topic | driving risk risk assessment virtual force modeling autonomous driving new energy vehicles |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2025.1658915/full |
| work_keys_str_mv | AT huilanli riskawarelocaltrafficsafetyevaluationforautonomousnewenergyvehiclesbasedonvirtualforcemodeling AT huilanli riskawarelocaltrafficsafetyevaluationforautonomousnewenergyvehiclesbasedonvirtualforcemodeling AT yunwan riskawarelocaltrafficsafetyevaluationforautonomousnewenergyvehiclesbasedonvirtualforcemodeling AT junningshang riskawarelocaltrafficsafetyevaluationforautonomousnewenergyvehiclesbasedonvirtualforcemodeling AT xiangyangxu riskawarelocaltrafficsafetyevaluationforautonomousnewenergyvehiclesbasedonvirtualforcemodeling |