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This paper presents an automated driving control algorithm for the control of an autonomous vehicle. In order to develop a highly automated driving control algorithm, one of the research issues is to determine a safe driving envelope with the consideration of probable risks. While human drivers maneuver the vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of that should be considered in determining a safe driving envelope. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope over a finite prediction horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles.

A methodology to design a model free cruise control algorithm(MFCC) is presented in this paper. General cruise control algorithms require lots of vehicle parameters to control the power train and the brake system, that makes control system complicate. Moreover, when the target vehicle is changed, the vehicle parameters should be reinvestigated in order to apply the cruise control algorithm to the subject vehicle. To overcome these disadvantages of the conventional cruise control algorithm, MFCC algorithm has been developed. The algorithm directly determines the throttle, brake inputs based on the reference model parameters such as clearance, relative velocity, and subject vehicle acceleration. This simple structure facilitates human centered design of cruise controller and makes it easy to apply control algorithm to various vehicles without reinvestigation of vehicle parameters.

This paper describes development and performance verification of a driving control algorithm for a 6 wheel driving and 6 wheel steering (6WD/6WS) vehicle using a real-time simulator. This control algorithm is developed to improve vehicle stability and maneuverability under high speed driving conditions. The driving controller consists of stability decision, upper, lower level and wheel slip controller. The stability decision algorithm determines desired longitudinal acceleration and reference yaw rate in order to maintain lateral and roll stability using G-vectoring method. Upper level controller is designed to obtain reference longitudinal net force, yaw moment and front/middle steering angles. The longitudinal net force is calculated to satisfy the reference longitudinal acceleration by the PID control theory. The reference yaw moment is determined to satisfy the reference yaw rate using sliding control theory. Lower level controller determines distributed tractive/braking torques.

This paper describes design and evaluation of a driving mode decision and lane change control algorithm of automated vehicle in merge situations on highway intersection. For the development of a highly automated driving control algorithm in merge situation, driving mode change from lane keeping to lane change is necessary to merge appropriately. In a merge situation, the driving objective is slightly different to general driving situation. Unlike general situation, the lane change should be completed in a limited travel distance in a merge situation. Merge mode decision is determined based on surrounding vehicles states and remained distance of merge lane. In merge mode decision algorithm, merge availability and desired merge position are decided to change lane safely and quickly. Merge availability and desired merge position are based on the safety distance that considers relative velocity and relative position of subject and surrounding vehicles.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

This paper proposes a rear-wheel steering control method that can modify and improve the vehicle lateral response without tire model and parameter. The proposed control algorithm is a combination of steady-state and transient control. The steady state control input is designed to modify steady-state yaw rate response of the vehicle, i.e. understeer gradient of the vehicle. The transient control input is a feedback control to improve the transient response when the vehicle lateral behavior builds up. The control algorithm has been investigated via computer simulations. Compared to classical control methods, the proposed algorithm shows good vehicle lateral response such as small overshoot and fast response. Specifically, the proposed algorithm can alleviate stair-shaped response of the lateral acceleration.

This paper describes a robust Model Predictive Control (MPC) framework of lane change for automated driving vehicles. In order to develop a safe lane change for automated driving, the driving mode and lane change direction are determined considering environmental information, sensor uncertainties, and collision risks. The safety margin is calculated using predicted trajectories of surround and subject vehicles. The MPC based combined steering and longitudinal acceleration control law has been designed with extended bicycle model over a finite time horizon. A reachable set of vehicle state is calculated on-line to guarantee that MPC state and input constraints are satisfied in the presence of disturbances and uncertainties. The performance of the proposed algorithm has been conducted simulation studies.