This thesis presents disturbance estimators and controllers for autonomous vehicles. In particular,

it focuses on a longitudinal distance controller and a lateral lane keeping controller.

First, in order to estimate road bank angle as a disturbance term in the lane keeping controller,

a kinematic relationship between road shape and sensor measurements was proposed.

Utilizing longitudinal and lateral vehicle dynamics, longitudinal road gradient and lateral

road bank angle were estimated simultaneously using the Unscented Kalman Filter (UKF)

approach. Second, a lane keeping controller associated with the road bank angle estimator

was proposed. For the controller, a steady state dynamic vehicle model was derived to

describe lateral vehicle dynamics. A Receding Horizon Sliding Control (RHSC) approach

was implemented to guarantee simple formulation and easy constraint consideration for the

receding horizon technique.

For the longitudinal control systems, the front vehicle's future motion was considered as a

disturbance term in a longitudinal distance controller for the ego vehicle. To predict the motion,

a new car-following model was proposed. To extract the current front vehicle driver's

driving style, a driver aggressivity factor was derived and estimated in real-time through the

UKF approach. Adopting a base car-following model and an aggressivity factor estimator on

the front vehicle, the front vehicle's future motion sequence was propagated. Furthermore,

as a distance controller associated with the front vehicle's future motion, a Fuel Eciency

Adaptive Cruise Control (ACC) was presented. A new fuel consumption model was included

in the optimization problem in order to improve fuel eciency. The nonlinear Model Predictive

Control approach was applied to the controller, and the front vehicle's future motion

was considered in the prediction horizon.

Two disturbance estimators for longitudinal and lateral motion were veried under simulation

and real vehicle tests in real-time. The lane keeping controller was proven to have

better performance with the bank angle estimator on public roads. Furthermore, for a distance

controller, fuel economy using a Fuel Eciency ACC has been veried in simulation.