Automated Lane Changing With a Controlled Steering-Wheel Feedback Torque for Low Lateral Acceleration Purposes

Abstract

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Formation Control of Connected Vehicles

“…It is worth mentioning that, although the merging scenario undoubtedly involves lateral (steering) dynamics, in this chapter we only consider the longitudinal dynamics involved in the merging scenario (gap creation and gap closing). While acknowledging the limits of this approach, we should note that this is in line with most literature on the topic, since synchronization for lateral dynamics poses problems that are still unsolved in the state of the art, such as handling non-holonomic constraints, avoiding corner cutting during turns, modelling lateral behaviour, studying disturbance propagation in the lateral direction [60,77]. As a matter of fact, even the winner of the latest GCDC 2016, team Halmstad, had no support for lateral control [6].…”

Cyclic Communication in Adaptive Strategies to Platooning: The Case of Synchronized Merging

“…Even when relying on the CACC protocol as in [31] (gap creation by slowly increasing the standstill distance) no parametric uncertainty nor well-posed control inputs are studied. In line with most literature, consider longitudinal dynamics only (gap creation and gap closing), since synchronization for lateral dynamics poses problems that are still unsolved, such as nonholonomic constraints, corner cutting during turns, disturbance propagation in the lateral direction, lateral behavior modeling [32], [33]. Note that even the winning team of GCDC 2016, team Halmstad, had no automated solution for lateral control [29].…”

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

“…In this Figure : a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; F yf is the lateral force of the front wheel; F yr is the lateral force of the rear wheel; v x is the longitudinal speed; v y is the lateral speed; ω is the angular velocity of transverse pendulum; β is the lateral deflection angle of the center of mass. The current research in the field of vehicle control focuses on establishing an efficient and reasonable lateral stability control strategy [3], and the main lateral control algorithms include classical PID (Proportional Integral Derivative) control methods [4], optimal preview control methods [5,6], robust control [7], sliding mode control methods [8], modern control algorithm MPC (Model Predictive Control) methods [9,10], fuzzy control methods [11], and so on, and the optimization strategies of various methods are innumerable. The literature uses lane line detection techniques combined with model predictive control to design controllers [12]; uses particle swarms to optimize higher-order sliding mode control parameters [13]; and designs controllers based on adaptive preview with directional error compensation [14].…”