Adaptive Cruise control (ACC) systems are nowadays used to increase safety in intelligent transportation systems. It exploits the advantages of various sensors for the acquisition and interpretation of the vehicles' environment. Sophisticated ACC functionalities, like collision avoidance on highways, require a high level of reliability and accuracy in the estimation of inter-vehicular distance. This distance can be measured by different type of sensors such as Lidar. In order to improve the reliability of the measured distance and adjust the speed of the ego vehicle, our strategy is to use all sources of information available in a collaborative approach, such as the transmitted speed and global positioning information from the front vehicle, and to validate the data given by these different information sources. To illustrate the potential of this approach, we present in this paper an example of a collaborative ACC developed on a French simulator Pro-SiVIC and discuss on its performance.