基于临界安全距离的自动超车路径规划和轨迹跟踪控制

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Juan Huang, Songlin Sun, Kai Long, Lairong Yin, Zhiyong Zhang
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引用次数: 0

摘要

自动驾驶汽车的超车过程必须优先考虑效率和安全,其中安全距离是一个关键参数。针对这一问题,我们提出了一种基于最小安全距离的自动超车路径规划方法,以确保操纵效率和安全性。该方法将恒速偏移模型的稳定运动和舒适性与正弦函数模型的平滑性相结合,创建了一个混合函数模型,可有效规划横向运动。为了进行精确的纵向运动规划,超车过程被分为五个阶段,并计算每个阶段的速度和行驶时间。为了增强控制系统,应用了模型预测控制(MPC)算法,建立了一个鲁棒的超车轨迹跟踪控制系统。数值模拟结果表明,所提出的超车路径规划方法可以生成平滑、连续的路径。在 MPC 框架下,自动驾驶汽车高效、安全地执行了自动超车操作,展示了该方法在提高自动驾驶系统性能和可靠性方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Overtaking Path Planning and Trajectory Tracking Control Based on Critical Safety Distance
The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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