Cooperative adaptive cruise control (CACC) is a potential solution to decrease traffic jams caused by shock waves, increase the road capacity, decrease fuel consumption and improve safety. This paper proposes an integrated solution to a combination of four challenges in these CACC systems. One of the technological challenges is how to guarantee string stability (the ability to avoid amplification of dynamic vehicle responses along the string of vehicles) under nominal operational conditions. The second challenge is how to apply this solution to heterogeneous vehicles. The third challenge is how to maintain confidentiality of the vehicle parameters. Finally, the fourth challenge is to find a method which improves robustness against wireless packet loss. This paper proposes a model predictive control approach in combination with a feed-forward control design, which is based on a shared vector of predicted accelerations over a finite time horizon. This approach is shown to be applicable to a heterogeneous sequence of vehicles, while the vehicle parameters remain confidential. In previous works such an approach has shown to increase robustness against packet losses. Conditions for string stability are presented for the nominal operational conditions. Experimental results are presented and indeed demonstrate string stable behavior.

Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension toward maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lane-reduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.

To improve traffic throughput, Cooperative Adaptive Cruise Control (CACC) has been proposed as a solution. The usage of Vehicle-to-Vehicle (V2V) communication enables short following distances, thereby increasing road capacity and fuel reduction (especially for trucks). Control designs for CACC use the wirelessly communicated intended acceleration of a preceding vehicle as a feedforward action in a following vehicle. This feedforward action may determine approximately 80% of the total control action. In case of a communication failure, this feedforward is no longer available, and a larger time gap is needed to ensure high performance and robustness in terms of stability and safety. However, such a larger time gap is not instantly realizable. Therefore, a CACC design is needed which is robust against intermittent communication failures. This paper proposes to share model-based predictions of the intended acceleration via V2V communication, which are stored in a buffer of the following vehicle. This buffer is used in case a packet dropout occurs. Further, since the communication frequency is lower than the frequency of the control-platform, this buffer is also used to virtually upgrade the communication frequency. The design has been tested in experimental vehicles and shows an increased control performance, also in periods of packet dropouts.