基于加权最优速度的自适应巡航车辆建模及仿真

Modeling and Simulation of Adaptive Cruise Control Vehicles Based on Weighted Optimal Velocity

摘要: 随着智能网联汽车快速发展，自适应巡航控制系统在提升驾驶安全性与舒适性方面发挥着重要作用。本文基于经典最优速度模型，提出一种考虑历史车间距信息的加权最优速度跟驰模型，该模型通过引入多历史时刻车间距的加权调节项，使车辆的加速度决策不仅依赖当前车间距，还参考过去一段时间内的车间距变化趋势，从而实现对交通流扰动的有效抑制。本文首先通过线性稳定性分析推导了模型的临界稳定性条件，随后在环形车道场景下开展数值仿真，验证了理论分析的正确性，研究结果表明：引入历史车间距信息能够显著扩大交通流的稳定区域。本文工作为自适应巡航系统的参数优化和控制策略设计提供了理论依据，有助于在缺失车车通信场景下提升交通流稳定性与通行效率。

Abstract: With the rapid development of intelligent and connected vehicles, Adaptive Cruise Control (ACC) systems play a significant role in enhancing driving safety and comfort. Based on the classical Optimal Velocity Model (OVM), this paper proposes a weighted optimal velocity car-following model that incorporates historical headway information. By introducing a weighted adjustment term of multi-step historical headway, the model enables the vehicle’s acceleration decision to depend not only on the current headway but also on the trend of headway variation over a past period, thereby effectively suppressing traffic flow disturbances. This paper first derives the critical stability condition of the model through linear stability analysis, and then conducts numerical simulations in a ring-road scenario to verify the correctness of the theoretical analysis. The results indicate that incorporating historical headway information can significantly expand the stable region of traffic flow. This work provides a theoretical basis for parameter optimization and control strategy design of ACC systems, contributing to the improvement of traffic flow stability and efficiency in the absence of Vehicle-to-Vehicle (V2V) communication.