福州大学学报（自然科学版）2026，Vol.54Issue(3)：309-314,6.DOI:10.7631/issn.1000-2243.25156
隧道段自动驾驶车道偏移预测方法
Research on lane departure prediction methods for autonomous vehicles in tunnel sections
摘要
Abstract
To address the issue of onboard vision perception caused by dynamic lighting conditions in tunnels,this paper proposes a lane departure prediction method for autonomous vehicles based on an adaptive Kalman filter.A coupled vehicle kinematics-vision perception model is established,integra-ting time-series data of multiple state variables such as lateral displacement and heading angle.Kalman filter is employed to achieve dynamic prediction.An adaptive adjustment mechanism is further designed to optimize filter parameters in real time,mitigating measurement noise induced by light flicker.Tests were conducted in tunnel scenarios constructed using the Prescan-Matlab/Simulink co-simulation platform.The results indicate that,compared with conventional detection methods,the proposed method provides earlier warning,higher correction efficiency,and adaptive capability to lighting fluctuations,demonstrating the robustness of the Kalman filter in complex tunnel lighting envi-ronments.关键词
自动驾驶车辆/车道偏移/隧道/视觉方案/卡尔曼滤波Key words
autonomous vehicle/lane shift/tunnel/visual scheme/Kalman filter分类
交通工程引用本文复制引用
王书易,陈葆靖,陈艳生,陈峰,傅丽碧,赖元文..隧道段自动驾驶车道偏移预测方法[J].福州大学学报（自然科学版）,2026,54(3):309-314,6.基金项目
国家自然科学基金资助项目(72474049) （72474049）
