Design of a Hierarchical Lane-Keeping Assist Control for a Steer-by-Wire Vehicle

The steer-by-wire (SBW) system has great potential in conjunction with advanced driver assistance systems (ADAS), due to the mechanical disconnection between the steering wheel and tires. However, the collaboration between the upper-level ADAS controller and the lower-level SBW controller needs to be addressed urgently. In this paper, a hierarchical lane-keeping assist (LKA) algorithm is proposed to concurrently optimize trajectory tracking performance and alleviate human-machine conflicts. Firstly, a vehicle dynamics model, a path following model and an SBW system model are developed for the lanekeeping and haptic feedback tasks. Then, linear time-varying (LTV) model predictive control (MPC) is designed to track the desired path and guarantee vehicle safety under the constraints of key states and controlled steering input in the upper-level controller. In the lower-level SBW controller, a torque overlay steering torque control based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">℞</sub> theory is developed to provide haptic feedback and track the commands from the upper-level controller. Finally, typical scenarios are given and the proposed strategies are validated using a CarSim & Simulink co-simulation platform as well as hardware-in-loop (HIL) bench. The results demonstrate that the proposed control scheme can improve the tracking performance while providing a driverfriendly interface.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Transportation Electrification
Published
2025-04-09
DOI
https://doi.org/10.1109/tte.2025.3559302
Citations
3
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
2.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of a Hierarchical Lane-Keeping Assist Control for a Steer-by-Wire Vehicle

Xuewu Ji, Yuewei Hu, Shuxin Tao, Yahui Liu et al.
3 citations
IEEE Transactions on Transportation Electrification
Vehicle Dynamics and Control Systems
2.05
article

Design of a Hierarchical Lane-Keeping Assist Control for a Steer-by-Wire Vehicle

Xuewu Ji, Yuewei Hu, Shuxin Tao, Yahui Liu, He Liu
article en
3 citations

Abstract

The steer-by-wire (SBW) system has great potential in conjunction with advanced driver assistance systems (ADAS), due to the mechanical disconnection between the steering wheel and tires. However, the collaboration between the upper-level ADAS controller and the lower-level SBW controller needs to be addressed urgently. In this paper, a hierarchical lane-keeping assist (LKA) algorithm is proposed to concurrently optimize trajectory tracking performance and alleviate human-machine conflicts. Firstly, a vehicle dynamics model, a path following model and an SBW system model are developed for the lanekeeping and haptic feedback tasks. Then, linear time-varying (LTV) model predictive control (MPC) is designed to track the desired path and guarantee vehicle safety under the constraints of key states and controlled steering input in the upper-level controller. In the lower-level SBW controller, a torque overlay steering torque control based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">℞</sub> theory is developed to provide haptic feedback and track the commands from the upper-level controller. Finally, typical scenarios are given and the proposed strategies are validated using a CarSim & Simulink co-simulation platform as well as hardware-in-loop (HIL) bench. The results demonstrate that the proposed control scheme can improve the tracking performance while providing a driverfriendly interface.

IEEE Transactions on Transportation ElectrificationVol. 11(4)
Tsinghua University (CN)
National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 14%
Vehicle Dynamics and Control Systems
2.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.