Model predictive control for unprotected left-turn based on sequential convex programming  

作　　者：Changlong Hao Yuan Zhang Yuanqing Xia 

机构地区：[1]School of Automation,Beijing Institute of Technology,Beijing,100081,China

出　　处：《Journal of Automation and Intelligence》2024年第4期230-239,共10页自动化与人工智能(英文)

基　　金：supported by the National Natural Science Foundation of China under Grant no.62373059;the Beijing Institute of Technology Research Fund Program for Young Scholars.

摘　　要：In autonomous driving,an unprotected left turn is a highly challenging scenario.It refers to the situation where there is no dedicated traffic signal controlling the left turns;instead,left-turning vehicles rely on the same traffic signal as the through traffic.This presents a significant challenge,as left-turning vehicles may encounter oncoming traffic with high speeds and pedestrians crossing against red lights.To address this issue,we propose a Model Predictive Control(MPC)framework to predict high-quality future trajectories.In particular,we have adopted the infinity norm to describe the obstacle avoidance for rectangular vehicles.The high degree of non-convexity due to coupling terms in our model makes its optimization challenging.Our way to solve it is to employ Sequential Convex Optimization(SCP)to approximate the original non-convex problem near certain initial solutions.Our method performs well in the comparison with the widely used sampling-based planning methods.

关 键 词：Autonomous driving DECISION-MAKING Planning and control Model predictive control OPTIMIZATION 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] U463.6[自动化与计算机技术—控制科学与工程]