协同自适应巡航控制
模型预测控制
巡航控制
计算机科学
稳健性(进化)
流量(计算机网络)
吞吐量
智能交通系统
排
自适应控制
计算机网络
控制(管理)
工程类
无线
运输工程
电信
人工智能
基因
化学
生物化学
作者
Mahdi Razzaghpour,Shahriar Shahram,Rodolfo Valiente,Yaser P. Fallah
标识
DOI:10.1109/vtc2021-fall52928.2021.9625177
摘要
Cooperative driving, enabled by communication between automated vehicle systems, is expected to significantly contribute to transportation safety and efficiency. Cooperative Adaptive Cruise Control (CACC) and platooning are two of the main cooperative driving applications that are currently under study. These applications offer significant improvements over current advanced driver assistant systems such as adaptive cruise control (ACC). The primary motivation of CACC and Platooning is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. These applications need an efficient controller to consider the computational cost and ensure driving comfort and high responsiveness. The advantage of Model Predictive Control is that we can realize high control performance since all constrains for these applications can be explicitly dealt with through solving an optimization problem. These applications highly depend on information updates and Communication reliability for their safety and stability purposes. In this paper, we propose a Model Predictive Control (MPC) based approach for CACC and platooning, and examine the impact of communication loss on the performance and robustness of the control scheme. The results show an improvement in response time and string stability, demonstrating the potential of cooperation to attenuate disturbances and improve traffic flow.
科研通智能强力驱动
Strongly Powered by AbleSci AI