Time filter
Source Type

Menhour L.,Universit& and x000E9 | d'Andrea-Novel B.,MINES ParisTech | Fliess M.,Ecole Polytechnique - Palaiseau | Gruyer D.,IFSTTAR CoSys LIVIC | Mounier H.,CNRS Laboratory of Signals & Systems
IEEE Transactions on Intelligent Transportation Systems | Year: 2017

In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed here. Let us point out that a ''good'' modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to ``intelligent'' controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this paper is dedicated to present simulation results with actual data. Actual data, used in MATLAB as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatness-based control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems. IEEE

Menhour L.,University of Technology of Troyes | D'Andrea-Novel B.,MINES ParisTech | Fliess M.,Ecole Polytechnique - Palaiseau | Gruyer D.,IFSTTAR CoSys LIVIC | Mounier H.,University Paris - Sud
2015 European Control Conference, ECC 2015 | Year: 2015

A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems. © 2015 EUCA.

Hoang T.B.,University Paris - Sud | Pasillas-Lepine W.,Supelec | De Bernardinis A.,IFSTTAR COSYS LTN | Netto M.,IFSTTAR COSYS LIVIC
IEEE Transactions on Control Systems Technology | Year: 2014

In the context of hybrid anti-lock brake systems, a closed-loop wheel-acceleration controller based on the observation of the extended braking stiffness (XBS) is provided. Its objective is to improve the system's robustness with respect to changes in the environment (as changes in road conditions, brake properties, etc.). The observer design is based on Burckhardt's tire model, which provides a wheel acceleration dynamics that is linear up to time-scaling. The XBS is one of the state variables of this model. This paper's main result is an observer that estimates this unmeasured variable. When the road conditions are known, a 3-D observer solves the problem. However, for unknown road conditions, a more complex 4-D observer must be used instead. In both the cases, the observer's convergence is analyzed using tools for switched linear systems that ensure uniform exponential stability (provided that a dwell-time condition is satisfied). Both experiments and simulations confirm the convergence properties predicted by the theoretical analysis. © 2012 IEEE.

IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2013

LIVIC-IFSTTAR develops driving assistance services in order to improve the driving safety. These systems are tested on several real prototypes equipped with sensors and perception, decision and control modules. But tests on real prototypes are not always available, effectively some hardware architectures could be too expensive to implement, scenario may lead to hazardous situations. Moreover, lots of reasons could lead to the inability to obtain both sensors and ground truth data for ADAS evaluation. However, safety applications must be tested in order to guaranty their reliability. For this task, simulation appears as a good alternative to the real prototyping and testing stages. In this context, the simulation must provide the same opportunities as reality, by providing all the necessary data to develop and to prototype different types of ADAS based on local or extended environment perception. The sensor data provided by simulation must be as noised and imperfect as those obtained with real sensors. To address this issue, the SiVIC platform has been developed; it provides a virtual road environment including realistic dynamic models of mobile entities (vehicles), realistic sensors, and sensors for ground truth. To test real embedded applications, an interconnection has been developed between SiVIC and third party applications (ie. RTMaps). In this way, the prototyped application can be directly embedded in real prototypes in order to test it in real conditions. A Full Speed Range ACC application is presented in this paper to illustrate the capabilities and the functionalities of this virtual platform. © 2013 IEEE.

Romon S.,CEREMA DTerSO | Bressaud X.,University Paul Sabatier | Lassarre S.,IFSTTAR COSYS GRETTIA | Saint Pierre G.,IFSTTAR COSYS LIVIC | Khoudour L.,CEREMA DTerSO
Journal of Navigation | Year: 2015

This article proposes a batch-mode algorithm to handle the large databases generated from experimentations using probe vehicles. This algorithm can locate raw Global Positioning System (GPS) positions on a map, but can also be used to correct map-matching errors introduced by real time map-matching algorithms. For each journey, the algorithm globally searches for the closest path to the GPS positions, and so is inspired from the path to path algorithm's family. It uses the Multiple Hypothesis Technique (MHT) and relies on an innovative weighting system based on the area between the GPS points and the arcs making up the path. For high performance, the algorithm uses an iterative program and the data is stored in tree form. Copyright © 2015 The Royal Institute of Navigation.

Transportation Research Part C: Emerging Technologies | Year: 2015

Recent improvements of communication technologies leads to several innovations in road vehicles energy consumption. As an example, several ecodriving applications already appeared on all smartphone application markets. Using embedded smartphone signals, such applications provide real time feedback to drivers according to their performances. However most of these applications does not take into account upcoming events such as curves, slopes or crossings to advise the driver on the best actions to undertake to lower energy consumption. Furthermore, they do not analyze data coming from vehicle sensors. In this paper, we present an android application, developed within the FP7 European project ecoDriver, which provides several innovative properties: advice according to upcoming events, a real time evaluation of the driving behavior, the analysis of past actions, an interface with OBD2 connector and some more. This paper further develops the complete architecture and links between each innovative function. Future works will concentrate on integrating image processing in this application in order to detect the possible presence of a front vehicle. © 2015 Elsevier Ltd.

Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 | Year: 2015

Reduced visibility on roadways caused by localized fog can impact the traffic flow in many ways: traffic speed, travel time delay, reduced capacity and accident risks. This paper presents a novel approach to estimate visibility conditions using an onboard camera and a digital map. Based on a traffic sign detector's characteristics in the fog, and registering detection by vision and information encoded in the map, we are able to accurately determine the current visual range in hazy conditions. Quantitative results are provided on a large experimental data set of driving environment with various level of fogginess. © 2015 MVA organization.

Loading IFSTTAR COSYS LIVIC collaborators
Loading IFSTTAR COSYS LIVIC collaborators