Open AccessLongitudinal and lateral control of autonomous vehicles in multi‐vehicle driving environments
Author(s) -
Wang Yulei,
Shao Qian,
Zhou Jian,
Zheng Hongyu,
Chen Hong
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0846
Subject(s) - trajectory , collision avoidance , control theory (sociology) , controller (irrigation) , vehicle dynamics , computer science , path (computing) , stability (learning theory) , lyapunov function , motion planning , collision , engineering , control (management) , simulation , automotive engineering , robot , nonlinear system , artificial intelligence , physics , computer security , astronomy , machine learning , quantum mechanics , agronomy , biology , programming language
Lane changes in multi‐vehicle driving environments are one of the most challenging manoeuvres for autonomous vehicles. The key innovation of this study is to develop an integrated longitudinal and lateral trajectory planning and tracking control algorithm under vehicle‐to‐vehicle communication. This algorithm includes two levels: trajectory planning and path‐following control. In the upper level, considering riding comfort, a collision‐free lane‐changing trajectory cluster is generated under different lane change durations. Then, the most appropriate trajectory from this cluster is provided by selecting the optimal lane change duration considering vehicle dynamics safety, collision avoidance of surrounding vehicles and driver preference. At the bottom level, a multiple‐input multiple‐output triple‐step non‐linear approach is proposed in the longitudinal and lateral path‐following controller design. The stability of the closed‐loop system is rigorously proven based on the Lyapunov function. Finally, the effectiveness of the proposed algorithm is verified with a high‐fidelity and full‐car model on the veDYNA platform.