An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles
An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles
复制标题
考虑多前后车信息的改进智能驾驶员模型
DOI：
10.1109/access.2021.3072058
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Meng Zeng
中科院分区：
文献类型：
--
作者：
Fang Zong;Meng Wang;Ming Tang;Xiying Li;Meng Zeng
This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for fleet control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for fleet control in the case of CAVs’ homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.
登录
查看更多内容
影响因子：
1.7
作者：
Saskia Ossen;S. Hoogendoorn
通讯作者：
Saskia Ossen;S. Hoogendoorn
影响因子：
2.7
作者：
CHANDLER, RE;HERMAN, R;MONTROLL, EW
通讯作者：
MONTROLL, EW
影响因子：
6.1
作者：
D. Scott;Sylvia Y. He
通讯作者：
D. Scott;Sylvia Y. He
基于支持向量机和数据去噪方案相结合的交通流预测
DOI：
10.1016/j.physa.2019.03.007
发表时间：
2019-11-15
影响因子：
3.3
作者：
Tang, Jinjun;Chen, Xinqiang;Li, Leixiao
通讯作者：
Li, Leixiao
DOI：
--
发表时间：
2006
期刊：
Journal of Central South Highway Engineering
影响因子：
--
作者：
Zhu Shanjiang
通讯作者：
Zhu Shanjiang