Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic
This paper presents an enhanced full velocity difference (FVD) model for characterising the car-following behaviour of autonomous vehicles (AVs). The model incorporates data from both front and rear vehicles, considering the subject vehicle’s velocity, the acceleration of many front and r...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10713363/ |
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| author | Boran Li Dongjie Xie Yiran Sun Jingze Ouyang |
| author_facet | Boran Li Dongjie Xie Yiran Sun Jingze Ouyang |
| author_sort | Boran Li |
| collection | DOAJ |
| description | This paper presents an enhanced full velocity difference (FVD) model for characterising the car-following behaviour of autonomous vehicles (AVs). The model incorporates data from both front and rear vehicles, considering the subject vehicle’s velocity, the acceleration of many front and rear vehicles, as well as the headway and velocity differential between the surrounding vehicles and the subject vehicle. Furthermore, the model incorporates location-related characteristics to precisely quantify the extent to which each front and rear vehicle’s influence changes based on its position relative to the subject vehicle. We determine the most suitable value for each parameter in the proposed model and analyse the effect of specific time delays on the stability of traffic flow. This analysis is based on data collected from a field test that involved a combination of human-driven vehicles (HDVs), AVs, and car-following technologies. Based on the results of the numerical simulation, the proposed model for AVs demonstrates a more seamless acceleration and deceleration compared to the FVD model. Moreover, the model can improve the stability of the mixed-vehicle platoon by augmenting the AV proportion. The model may also be utilised to replicate the behaviour of HDVs and AVs following cars in traffic that consists of a mix of different vehicle types. This capability is beneficial for managing road traffic and designing infrastructure. |
| format | Article |
| id | doaj-art-c6b3c456d78b4c01a1485b565cbfe34a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c6b3c456d78b4c01a1485b565cbfe34a2025-08-20T03:07:06ZengIEEEIEEE Access2169-35362025-01-0113834708348210.1109/ACCESS.2024.347752910713363Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous TrafficBoran Li0Dongjie Xie1https://orcid.org/0000-0002-8765-4087Yiran Sun2Jingze Ouyang3Yunnan Land and Resources Vocational College, Kunming, ChinaSchool of Computing, Hunan University of Technology, Zhuzhou, Hunan, ChinaFaculty of Construction Engineering, Dalian University of Technology, Dalian, ChinaSchool of Computing, Hunan University of Technology, Zhuzhou, Hunan, ChinaThis paper presents an enhanced full velocity difference (FVD) model for characterising the car-following behaviour of autonomous vehicles (AVs). The model incorporates data from both front and rear vehicles, considering the subject vehicle’s velocity, the acceleration of many front and rear vehicles, as well as the headway and velocity differential between the surrounding vehicles and the subject vehicle. Furthermore, the model incorporates location-related characteristics to precisely quantify the extent to which each front and rear vehicle’s influence changes based on its position relative to the subject vehicle. We determine the most suitable value for each parameter in the proposed model and analyse the effect of specific time delays on the stability of traffic flow. This analysis is based on data collected from a field test that involved a combination of human-driven vehicles (HDVs), AVs, and car-following technologies. Based on the results of the numerical simulation, the proposed model for AVs demonstrates a more seamless acceleration and deceleration compared to the FVD model. Moreover, the model can improve the stability of the mixed-vehicle platoon by augmenting the AV proportion. The model may also be utilised to replicate the behaviour of HDVs and AVs following cars in traffic that consists of a mix of different vehicle types. This capability is beneficial for managing road traffic and designing infrastructure.https://ieeexplore.ieee.org/document/10713363/Car-following behaviormixed traffic flowmulti-front and rear vehiclesstabilityautonomous vehicles |
| spellingShingle | Boran Li Dongjie Xie Yiran Sun Jingze Ouyang Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic IEEE Access Car-following behavior mixed traffic flow multi-front and rear vehicles stability autonomous vehicles |
| title | Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic |
| title_full | Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic |
| title_fullStr | Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic |
| title_full_unstemmed | Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic |
| title_short | Enhanced Car-Following Model Incorporating Multi-Vehicles Information in Heterogeneous Traffic |
| title_sort | enhanced car following model incorporating multi vehicles information in heterogeneous traffic |
| topic | Car-following behavior mixed traffic flow multi-front and rear vehicles stability autonomous vehicles |
| url | https://ieeexplore.ieee.org/document/10713363/ |
| work_keys_str_mv | AT boranli enhancedcarfollowingmodelincorporatingmultivehiclesinformationinheterogeneoustraffic AT dongjiexie enhancedcarfollowingmodelincorporatingmultivehiclesinformationinheterogeneoustraffic AT yiransun enhancedcarfollowingmodelincorporatingmultivehiclesinformationinheterogeneoustraffic AT jingzeouyang enhancedcarfollowingmodelincorporatingmultivehiclesinformationinheterogeneoustraffic |