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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10713363/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |