Cooperative adaptive cruise control (CACC) makes the vehicle follow its predecessor at a close but safe distance, and uses information received from other vehicles to accomplish this task. In literature and in practice, the control method mostly applied for CACC is proportional integral derivative (PID) control, possibly with some refinement for gear shifting or comfort. The control method called model predictive control (MPC) can also be used for CACC, and from literature it appears to be more promising than PID, because of its ability to anticipate future situations and to implement constraints directly into the control algorithm. MPC applies the first input of a control input sequence that optimises a performance index calculated from predicted system behaviour, based on a prediction model, subject to operational constraints, in a receding horizon approach. Furthermore, literature has shown that with PID the use of state information from the second predecessor or the platoon leader, in addition to the direct predecessor’s states, can improve the CACC performance. Therefore, in this thesis the approach of using such additional communicated information from either the second predecessor or the platoon leader is combined with the use of MPC as control method. The goal is to investigate whether any of these two configurations give an increase in performance compared with similar configurations with PID as control method, and compared with a more basic configuration that uses just the direct predecessor’s state information with either MPC or PID. Also, the possibly added value of using communicated predicted states, in addition to current states, with MPC is investigated. The CACC controllers are designed to control the throttle, the brakes, and the gears, subject to operational constraints on acceleration, velocity, and vehicle-to-vehicle distance. The PID-based CACC controller contains a proportional feedback of the errors in velocity, position, and acceleration, combined with an automatic transmission scheme, and the control input is restricted at time instants at which a constraint is (almost) violated. The MPC-based CACC controller at each time step minimises the expected errors in position and velocity and the corresponding input variation. The MPC prediction model is obtained by approximating a nonlinear vehicle model by a piecewise affine (PWA) model, and converting the MPC optimisation problem into a mixed integer linear programming (MILP) problem. In this project, tuning is done by applying simulated annealing for a scenario involving four CACC-controlled vehicles following a platoon leader. Then, the tuned controllers are implemented in a validation scenario comprising a larger platoon undergoing a longer simulation. The results from simulating this validation scenario show that the PID-based CACC controller has a low responsiveness, compared with MPC, and lets the first two vehicles crash. With MPC several peaks and oscillations in throttle/brake input and acceleration occur, and it is expected that with the MPC-based CACC controllers as designed and tuned here, string stability will not always be achieved for increasing platoon lengths. It is expected that properly retuning will result in better performing controllers. However, due to limited time, this retuning could not be performed within the scope of this project, and is therefore left as a recommendation. Therefore, only preliminary conclusions can be formulated, which are that MPC should be preferred over PID as a control method for CACC, because it is safer. Moreover, with MPC it should be preferred to, in addition to the current states of the direct predecessor, at least use the current states of the second predecessor and/or the predicted future states from the direct predecessor, in order to achieve better string stability.