No abstract

This paper presents the construction of an aggregated indicator of a fuelefficient driving style, in order to construct an efficient Ecological Driving Assistance System (EDAS). Such an eco-index can be used to detect eco-driving behaviour, but also to give to the driver useful advices to help him improving his driving efficiency without deteriorating safety. The logistic regression is used to model our experimental dataset of twenty subjects driving twice the same route: normally or following the golden rules of eco-driving. Depending on some driving indicators, the estimated probability of being an eco-driver is used as an eco-index to characterize that driving pattern. This work show how such a simple aggregated indicator, related to driving dynamics rather than fuel consumption, can be useful for driver monitoring and information. Two models, from the simplest to the most complicated, are compared, and their performances analysed.

Individual space-speed profiles are very informative to study drivers behavior and their road usage. Due to uncertainties in the measurements of position and speed from sensors, there is need of use a smoothing procedure in order to estimate these space-speed profiles. The purpose of this study is to propose a smoothing method based on a functional approach. This approach take inspiration from Functional Data Analysis, a statistical domain that has developed recently. So, we define and study some properties of the functional space of spacespeed profiles. Then, due to the correspondence between speed and position (and implicitly time), we prove that a change of study area (distance vs time) is more appropriate and leads to a smoothing problem under monotonicity constraint. An estimation method of monotone smoothing spline is used and tested with simulated data.

No abstract

No abstract