Abstract
Keywords - Drivers´ profiles, driver´s behavior, principal components analysis, k-means clustering
Over the past 15 years major technological changes have taken place in the field of automotive industry. Advanced driver assistance systems (ADAS) are fitted in more and more cars. The design of useful and safe ADAS requires real driving behavior data in particular for their specification and their tune-up. These systems, such as Adaptive Cruise Control (ACC), use for their functioning behavioral data (actions of the driver), vehicle dynamic data (speed, acceleration...) as well as information about close traffic in longitudinal regulation situations. To better improve the specification of these systems, drivers´ profiles allow to make ADAS functioning suitable with driving task requirements. An experiment on open road was carried out with 126 drivers on an instrumented car non equipped with ACC. To ensure that representative road situations are taken into account, data has been recorded in ecological conditions, with drivers on a 250 km real road. Four data types were recorded: drivers´ actions, their comments, car dynamic and road environment characteristics. Four main situations of driving (car following, overtaking, cut-in of another vehicle and insertion on highways) have been studied. Some other particular situations were also recorded in order to allow describing the driving in a microscopic way. Two levels of indicators have been calculated: macroscopic ones (mean highways speed...) and microscopic ones (headways before overtaking...). We focus in this paper on the methodology elaborated to extract relevant driving indicators, useful to determine drivers´ profiles. Several methods of multidimensional exploratory data analysis like Principal Components Analysis (PCA), kmeans Clustering allow to characterize five clusters of drivers. 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. The data collected from this study and from other ones should enable determining rules for the specification of Adaptive Cruise Control adaptive to drivers´ profiles.