Evaluation of ecodriving performances and teaching method: comparing training and simple advice

Eco-driving style is widely known to induce up to 20% fuel consumption reduction, but little is known about differences due to different learning methods. In order to evaluate the potential impacts of future ecological driving assistance system (EDAS) in comparison with usual techniques, a statistical approach is proposed. Two kinds of experiments are analysed in this paper: In the first one, simple advice were given to the participants, while in the second one, full courses with eco-driving experts were used. Performance indicators were derived from five commonly referred golden rules of eco-driving and used as model inputs. Different kind of statistical models are discussed, among which we choose to apply the ordinary logistic regression to assess the effects of each driving advice separately Results show that ecodriving advices are better applied after a course than just providing tips. The approach is then extended to build a generic model that can be used both to characterize and evaluate eco-driving style

A moving fixed-interval filter/smoother for estimation of vehicle position using odometer and map-matched GPS

This paper presents some optimal real-time and post-processing estimators of vehicle position using odometer and map-matched GPS measurements. These estimators were based on a simple statistical error model of the odometer and the GPS which makes the model generalizable to other applications. Firstly, an asymptotically minimum variance unbiased estimator and two optimal moving fixed interval filters which are more flexibles are exposed. Then, the post-processing case leads to the construction of two moving fixed interval smoothers. These estimators are tested and compared with the classical Kalman filter with simulated and real data, and the results show a good accuracy of each of them

A functional analysis of speed profiles: smoothing using derivative information, curve registration, and functional boxplot

In this paper, we propose a functional analysis of a set of individual space-speed profiles corresponding to speed as function of the distance traveled by the vehicle from an initial point. This functional analysis begins with a functional modeling of space-speed profiles and the study of mathematical properties of these functions. Then, in a first step, a smoothing procedure based on spline smoothing is developed in order to convert the raw data into functional objets and to filter out the measurement noise as efficiently as possible. It is shown that this smoothing step leads to a complex nonparametric regression problem that needs to take into account two constraints: the use of the derivative information, and a monotonicity constraint. The performance of the proposed two-step estimator (smooth, and then monotonize) is illustrated on simulation studies and a real data example. In a second step, we use a curve registration method based on landmarks alignment in order to construct an average speed profile representative of a set of individual speed profiles. Finally, the variability of such a set is explored by the use of functional boxplots

Rapport d’expertise de la procédure de qualification des équipements ILS. Limites et Probabilités de confiance.

L'objet de cette étude est de clarifier certains passages du rapport "DERA/WSS/WX1/CR 980799/2.3 ILS Certification Requirements". Nous étudions en particulier le paragraphe 3.3 "Confidence Limits for Sequential Tests" p.28-34 et le paragraphe 2 de l'appendice D, p.69-72

Study of the ILS certification process. Confidence limits and probabilities.

The purpose of this study is to explain some parts of the DERA report DERA/WSS/WX1/CR 980799/2.3. "ILS Certification Requirements" (Ref. 2). We mainly study section 3.3 "Confidence Limits for Sequential Tests" p.28-34 and section 2 of the appendix D, p.69-72

Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest

As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles

Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

International audienceThe increasing availability of large-scale Global Positioning System (GPS) data stemming from in-vehicle embedded terminal devices enables the design of methods deriving road network cartographic information from drivers' recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-meter-long road span. We proposed three different ways of deriving features: the first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that Random Forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs

D41.1 : Performance Indicators and ecoDriver Test Design

This deliverable details the proposed assessment approaches and the design of field trials for data provision. Research questions and objectives of the project were divided into three major themes: user acceptance, behaviour, as well as energy use and emissions, which led to the formation of 24 hypotheses in total. A large number of Performance Indicators were identified, which will be used to validate the hypotheses. These Performance Indicators were grouped into 16 categories, covering the aforementioned three research themes. To provide empirical data for validating the hypotheses and answering the research questions, a series of field trials will betaken place in SP3. There are 12 fleets of vehicles, across 7 countries and covering a wide range of vehicle types. This deliverable outlines experimental design of the field trials, including fleet specifications, participant recruitment, route selection, test procedures, and data collection protocol etc. There are similarities but also individual characteristics of these experimental designs across the fleets and test sites, in order to produce all necessary data for addressing the research question

Ventricular fibrillation in experimental hypothermic cardiac surgery, the evaluation of antifibrillary agents

Thesis (M.A.)--Boston Universit