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Research on Mixed Traffic Flow Model of Autonomous-Manual Driving Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published December 30, 2020 by SAE International in United States
Annotation ability available
Event: 3rd International Forum on Connected Automated Vehicle Highway System through the China Highway & Transportation Society
With the rapid development of science and technology, autonomous driving vehicle has become a research hot spot in the automotive industry, and it also has a great impact on the transportation system. The driving mode of autonomous driving vehicle is more convenient and efficient. The transition from manual driving vehicle to autonomous driving vehicle is changing the state of traditional traffic system. The addition of autonomous driving vehicle can alleviate traffic congestion and improve road capacity. Based on the cellular automata theory and multi-agent theory, in connected intelligent traffic system (ITS), this paper constructs a simulation model of mixed traffic flow of autonomous-manually driving vehicle agent by adopting different car-following rules for autonomous driving vehicles and manual driving vehicles, and analyzes the impact of mixing ratio and reaction time on mixed traffic flow in free-flow, steady-flow and blocked-flow respectively. The results show that, when in free-flow, under the influence of drivers' psychological factors, manual driving vehicles have a high velocity, which can improve the road capacity. With the increase of the proportion of autonomous vehicles in steady-flow and blocked-flow, the mitigation effect of autonomous vehicles on traffic flow is more obvious. At the same time, the reaction time of the autonomous driving vehicle system also has a significant impact on the road capacity. When the reaction time is equal to 0s, compared with 1s, the average velocity increased up to 38% and the traffic flow increased up to 94%. Increasing the proportion of autonomous vehicles or reducing reaction time can alleviate traffic conditions.
- You Ren - Jilin University, China
- Liangzhe Wang - Jilin University, China
- Guan Yan - Jilin University, China
- Hongmei Shan - Jilin University, China
- Huiying Lin - Jilin University, China
- Zhilong Zhang - Jilin University, China
- Shan Jiang - Jilin University, China
- Xuesheng Zheng - Jilin University, China
- Jiaqi Song - Jilin University, China
CitationRen, Y., Wang, L., Yan, G., Shan, H. et al., "Research on Mixed Traffic Flow Model of Autonomous-Manual Driving Vehicles," SAE Technical Paper 2020-01-5146, 2020, https://doi.org/10.4271/2020-01-5146.
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