Recent cars are more and more equipped with advanced driver assistance systems (ADAS). The design of useful and safe ADAS requires real driving behavior data in particular for their specification and their tune-up. Our study is focused on the improvement of adaptive cruise control (ACC) design. The specification of such a system requires drivers’ profiles using driver’s actions and vehicle dynamic data (speed, acceleration…) as well as information about close traffic in longitudinal regulation situations. An experiment on real road is currently carried out with 120 common subjects driving an instrumented car. To ensure that representative road situations are taken into account, data are recorded in ecological conditions, with common drivers using a non-ACC equipped car on a 250 km real road. Four data types are recorded: drivers’ actions and comments, car dynamic and road environment characteristics. Drivers’ profiles presented in this paper are based on objective data like headways or speed choices in some relevant driving situations. This experimental method has the advantage to allow understanding both the driver’s real need (and not what the technology enables) and his/her real dynamic use of the car. As for any experimental procedure, it is essential to be aware of some biases which could impact the study conclusions. The data collected from this study and also from other ones should enable building an “intelligent” driving algorithm able to classify any driver in a pre-defined category of profile in order to configure automatically the best ACC functioning mode.