3 research outputs found
A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles
(AVs) and connected and automated vehicles (CAVs), and it is paramount in
vehicle safety, passenger comfort, transportation efficiency, and energy
saving. This survey attempts to provide a comprehensive and thorough overview
of the current state of vehicle control technology, focusing on the evolution
from vehicle state estimation and trajectory tracking control in AVs at the
microscopic level to collaborative control in CAVs at the macroscopic level.
First, this review starts with vehicle key state estimation, specifically
vehicle sideslip angle, which is the most pivotal state for vehicle trajectory
control, to discuss representative approaches. Then, we present symbolic
vehicle trajectory tracking control approaches for AVs. On top of that, we
further review the collaborative control frameworks for CAVs and corresponding
applications. Finally, this survey concludes with a discussion of future
research directions and the challenges. This survey aims to provide a
contextualized and in-depth look at state of the art in vehicle control for AVs
and CAVs, identifying critical areas of focus and pointing out the potential
areas for further exploration
IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics
The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers