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This paper is on the automated driving architecture and operation of a light commercial vehicle. Simple longitudinal and lateral dynamic models of the vehicle and a more detailed CarSim model are developed and used in simulations and controller design and evaluation. Experimental validation is used to make sure that the models used represent the actual response of the vehicle as closely as possible. The vehicle is made drive-by-wire by interfacing with the existing throttle-by-wire, by adding an active vacuum booster for brake-by-wire and by adding a steering actuator for steer-by-wire operation. Vehicle localization is achieved by using a GPS sensor integrated with six axes IMU with a built-in INS algorithm and a digital compass for heading information. Front looking radar, lidar and camera are used for environmental sensing. Communication with the road infrastructure and other vehicles is made possible by a vehicle to vehicle communication modem. A dedicated computer under real time Linux is used to collect, process and distribute sensor information. A dSPACE MicroAutoBox is used for drive-by-wire controls. CACC based longitudinal control and path tracking of a map of GPS waypoints are used to present the operation of this automated driving vehicle.

Vehicle stability control systems are important components of active safety systems for road transport. The problem of vehicle lateral stability control is addressed in this paper using active front wheel steering and individual braking. Vehicle lateral stability control means keeping the vehicle yaw rate and the vehicle side slip angle in desired values. For this reason, a model-based controller is designed. The desired yaw rate is obtained from the single track vehicle model and the desired side slip angle is chosen as zero. Controller design consists of two parts, lower and upper controller parts. Upper controller is designed based on Model Predictive Control (MPC) method. This upper controller changes front wheel steering angles utilizing steer-by-wire system and also it generates the required control moment for stabilizing the yaw motion of the vehicle. Lower controller is an individual braking algorithm. It determines the individual braking wheel. In this way, the control moment can be applied to the vehicle. The designed controller is tested using the nonlinear single track vehicle model and the higher fidelity CarMaker vehicle model.