Autonomous vehicle technology has been developing rapidly in recent years. Vehicle parametric uncertainty in the vehicle model, variable time delays in the CAN bus based sensor and actuator command interfaces, changes in vehicle sped, sensitivity to external disturbances like side wind and changes in road friction coefficient are factors that affect autonomous driving systems like they have affected ADAS and active safety systems in the past. This paper presents a robust control architecture for automated driving systems for handling the above mentioned problems. A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject yaw moment disturbances . The compensation of vehicle model with the embedded disturbance observer forces it to behave like its nominal model within the bandwidth of the disturbance observer. A parameter space approach based steering controller is then used to optimize performance. The proposed method demonstrates good disturbance rejection and achieves stability robustness. The variable time delay from the “steer-by-wire” system in an actual vehicle can also lead to stability issues since it adds large negative phase angle to the plant frequency response and tends to destabilize it. A communication disturbance observer (CDOB) based time delay compensation approach that does not require exact knowledge of this time delay is embedded into the steering actuation loop to handle this problem. Stability analysis of time delay compensation approach by CDOB is presented in this paper. Extensive model-in-the-loop and hardware-in-the-loop (HiL) simulations are performed to test the designed disturbance observer and CDOB systems and show reduced path following errors in the presence of uncertainty and disturbances. A validated model of our 2017 Ford Fusion Hybrid research autonomous vehicle is used in the simulation analyses. The HiL simulator that uses a validated CarSim model with sensors and traffic is also used to verify the real time capability of our approach.