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8 results on '"AKKA Technologies"'

1. 3D Surface Reconstruction from Voxel-based Lidar Data

Author

Raoul de Charette, Luis Roldão, Anne Verroust-Blondet, Robotics & Intelligent Transportation Systems (RITS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AKKA Technologies, CIFRE AKKA Technologies, and Roldao Jimenez, Luis Guillermo

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Computer science, Computer Vision and Pattern Recognition (cs.CV), Gaussian, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, [INFO] Computer Science [cs], computer.software_genre, Computer Science - Robotics, symbols.namesake, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020901 industrial engineering & automation, Voxel, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Lidar data, Computer vision, [INFO]Computer Science [cs], ComputingMethodologies_COMPUTERGRAPHICS, business.industry, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Kernel (image processing), symbols, Artificial intelligence, business, Robotics (cs.RO), computer, Surface reconstruction
Abstract

To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding. In this paper, a 3D surface reconstruction algorithm from heterogeneous density 3D data is presented. The proposed method is based on a TSDF voxel-based representation, where an adaptive neighborhood kernel sourced on a Gaussian confidence evaluation is introduced. This enables to keep a good trade-off between the density of the reconstructed mesh and its accuracy. Experimental evaluations carried on both synthetic (CARLA) and real (KITTI) 3D data show a good performance compared to a state of the art method used for surface reconstruction., IEEE Intelligent Transportation Systems Conference (ITSC) 2019

Published
2019

2. Pre-flight Calibration and Near-Earth Commissioning Results of the Mercury Plasma Particle Experiment (MPPE) Onboard MMO (Mio)

Author

Jean-Jacques Berthelier, Henning Fischer, Mathieu Petiot, Kazushi Asamura, Eric Le Comte, Zdenĕk Nĕmec̆ek, Andrei Fedorov, Giuseppe Consolini, Yoichi Kazama, Haruhisa Matsumoto, Yoshizumi Miyoshi, Christian Mazelle, Kanako Seki, Jean Rouzaud, Christophe Verdeil, Dominique Delcourt, Wataru Miyake, Harald Michalik, F. Leblanc, Björn Fiethe, Ulrich Bührke, Roberto Bruno, Peter Wurz, Iannis Dandouras, Alain Barthe, Henry-Claude Séran, Jean-André Sauvaud, Maria Federica Marcucci, Mats Holmström, Stas Barabash, Shoichiro Yokota, H. Andersson, Joachim Woch, Masahiro Hoshino, Iku Shinohara, Philippe Louarn, Benoit Lavraud, Dominique Fontaine, Vincent Génot, Alexandre Cadu, Yuki Harada, Christian Jacquey, Anne-Marie Frezoul, Andrew J. Coates, Bruno Katra, Yoshifumi Futaana, Emmanuel Penou, Stefan Karlsson, Jean-Marie Illiano, Markus Fraenz, Claude Aoustin, Qiugang Zong, Catherine Garat, Qiu-Mei Lee, Wing-Huen Ip, Manabu Shimoyama, Masaki N. Nishino, David Moirin, Norbert Krupp, Nicolas André, Masaki Fujimoto, Mitsuo Oka, Hiroshi Hasegawa, Martin Wieser, Guy Peyre, Jana Safrankova, Jean-Louis Médale, Kunihiro Keika, Jean-Denis Techer, Takeshi Takashima, T. Yanagimachi, Naoki Terada, Takefumi Mitani, Masafumi Hirahara, Yoshifumi Saito, Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency [Sagamihara] (JAXA), Laboratoire de Physique des Plasmas (LPP), Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institute for Space-Earth Environmental Research [Nagoya] (ISEE), Nagoya University, Swedish Institute of Space Physics [Kiruna] (IRF), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Osaka University [Osaka], Space Sciences Laboratory [Berkeley] (SSL), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Kyoto University, AKKA Technologies, Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Société MICROTEC, Nexeya Conseil & Formation, COMAT, Faculty of Mathematics and Physics [Charles University of Praha], Charles University [Prague] (CU), Istituto di Astrofisica e Planetologia Spaziali - INAF (IAPS), Istituto Nazionale di Astrofisica (INAF), Tokai University, The University of Tokyo (UTokyo), Mullard Space Science Laboratory (MSSL), University College of London [London] (UCL), HELIOS - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Max-Planck-Institut für Sonnensystemforschung = Max Planck Institute for Solar System Research (MPS), Max-Planck-Gesellschaft, Institute of Computer and Network Engineering [Braunschweig] (IDA), Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig], Japan Aerospace Exploration Agency [Tsukuba] (JAXA), Rikkyo University [Tokyo], Peking University [Beijing], University of Bern, Academia Sinica Institute of Astronomy and Astrophysics (ASIAA), Academia Sinica, National Central University [Taiwan] (NCU), Tohoku University [Sendai], Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), University of California [Berkeley], University of California-University of California, Kyoto University [Kyoto], Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), and Max-Planck-Institut für Sonnensystemforschung (MPS)

Subjects
Spectrum analyzer, High energy particle, 010504 meteorology & atmospheric sciences, [PHYS.ASTR.EP]Physics [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP], chemistry.chemical_element, 7. Clean energy, 01 natural sciences, law.invention, Orbiter, law, 0103 physical sciences, Calibration, Aerospace engineering, 010303 astronomy & astrophysics, 0105 earth and related environmental sciences, [PHYS]Physics [physics], Energetic neutral atom, Spacecraft, business.industry, Astronomy and Astrophysics, Mercury (element), Solar wind, chemistry, 13. Climate action, Space and Planetary Science, [SDU]Sciences of the Universe [physics], Environmental science, business, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

International audience; BepiColombo Mio (previously called MMO: Mercury Magnetospheric Orbiter) was successfully launched by Ariane 5 from Kourou, French Guiana on October 20, 2018. The Mercury Plasma/Particle Experiment (MPPE) is a comprehensive instrument package onboard Mio spacecraft used for plasma, high-energy particle and energetic neutral atom measurements. It consists of seven sensors including two Mercury Electron Analyzers (MEA1 and MEA2), Mercury Ion Analyzer (MIA), Mass Spectrum Analyzer (MSA), High Energy Particle instrument for electron (HEP-ele), High Energy Particle instrument for ion (HEP-ion), and Energetic Neutrals Analyzer (ENA). Significant efforts were made pre-flight to calibrate all of the MPPE sensors at the appropriate facilities on the ground. High voltage commissioning of MPPE analyzers was successfully performed between June and August 2019 and in February 2020 following the completion of the low voltage commissioning in November 2018. Although all of the MPPE analyzers are now ready to begin observation, the full service performance has been delayed until Mio’s arrival at Mercury. Most of the fields of view (FOVs) of the MPPE analyzers are blocked by the thermal shield surrounding the Mio spacecraft during the cruising phase. Together with other instruments on Mio including Magnetic Field Investigation (MGF) and Plasma Wave Investigation (PWI) that measure plasma field parameters, MPPE will contribute to the comprehensive understanding of the plasma environment around Mercury when BepiColombo/Mio begins observation after arriving at the planet Mercury in December 2025.

Published
2021
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3. A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks

Author

Bernard Archimde, Xavier Desforges, Mickal Divart, AKKA Technologies (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
Complex systems, 0209 industrial biotechnology, Decision support system, Engineering, General Computer Science, Generic function, media_common.quotation_subject, 0211 other engineering and technologies, Complex system, Système multi-agents, 02 engineering and technology, Preventive maintenance, 020901 industrial engineering & automation, Availability assessment, Function (engineering), Prognostics, media_common, 021103 operations research, business.industry, Node (networking), General Engineering, Production planning, Modélisation et simulation, Reliability engineering, business
Abstract

The paper proposes a modeling of complex systems to prognose their ability to complete future tasks from local prognostics.The paper presents the decision support indicators.The paper presents inferences to compute the decision support indicators for each node of the model.The general algorithm of the prognostic function is then given.The paper shows how the computed decision support indicators can be used for production and maintenance joint planning. The high costs of complex systems lead companies to improve their efficiency. This improvement can particularly be achieved by reducing their downtimes because of failures or for maintenance purposes. This reduction is the main goal of Condition-Based Maintenance and of Prognostics and Health Management. Both those maintenance policies need to install appropriate sensors and data processes not only to assess the current health of their critical components but also their future health. These future health assessments, also called prognostics, produce the Remaining Useful Life of the components associated to imprecision quantifications. In the case of complex systems where components are numerous, the matter is to assess the health of whole systems from the prognostics of their components (the local prognostics). In this paper, we propose a generic function that assesses the future availability of complex systems from their local prognostics (the prognostics of their components) by using inferences rules. The results of this function can then be used as decision support indicators for planning productive and maintenance tasks. This function exploits a proposed extension for Object Oriented Bayesian Networks (OOBN) used to model the complex system in order to assess the probabilities of failure of components, functions and subsystems. The modeling of the complex system is required and it is presented as well as modeling transformations to tackle some OOBN limitations. Then, the computing inference rules used to define the future availability of complex systems are presented. The extension added to OOBN consists in indicating the components that should first be maintained to improve the availabilities of the functions and subsystems in order to provide a second kind of decision support indicators for maintenance. A fictitious multi-component system bringing together most of the structures encountered in complex systems is modeled and the results obtained from the application of the proposed generic function are presented as well as ways that production and maintenance planning can used the computed indicators. Then we show how the proposed generic prognostic function can be used to predict propagations of failures and their effects on the functioning of functions and subsystems.

Published
2017
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4. LMSCNet: Lightweight Multiscale 3D Semantic Completion

Author

Luis Roldão, Anne Verroust-Blondet, Raoul de Charette, Roldao Jimenez, Luis Guillermo, Robotics & Intelligent Transportation Systems (RITS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and AKKA Technologies

Subjects
FOS: Computer and information sciences, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 010501 environmental sciences, [INFO] Computer Science [cs], Semantics, 01 natural sciences, Lidar, Convolutional code, Feature (computer vision), 3d segmentation, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Benchmark (computing), 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Artificial intelligence, business, Algorithm, 0105 earth and related environmental sciences
Abstract

We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehensive multiscale skip connections to enhance feature flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our method performs on par on semantic completion and better on occupancy completion than all other published methods -- while being significantly lighter and faster. As such it provides a great performance/speed trade-off for mobile-robotics applications. The ablation studies demonstrate our method is robust to lower density inputs, and that it enables very high speed semantic completion at the coarsest level. Our code is available at https://github.com/cv-rits/LMSCNet., Accepted at 3DV 2020 (Oral). For a demo video, see http://tiny.cc/lmscnet. Code is available at https://github.com/cv-rits/LMSCNet

Published
2020

5. A Semi-automated Requirements Reuse and Recycling Process for Autonomous Transportation Systems R&D

Author

Yann Leroy, Marija Jankovic, Youssef Damak, Karim Chelbi, Laboratoire Génie Industriel - EA 2606 (LGI), CentraleSupélec, and AKKA Technologies

Subjects
0209 industrial biotechnology, Process modeling, Traceability, Information technology consulting, Process (engineering), business.industry, Computer science, 020207 software engineering, Context (language use), 02 engineering and technology, General Medicine, Reuse, Intellectual property, [SPI]Engineering Sciences [physics], 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, Use case, Software engineering, business, ComputingMilieux_MISCELLANEOUS
Abstract

The R&D of Autonomous Transportation Systems (ATS) is hindered by the lack of industrial feedback and client's knowledge about technological possibilities. In addition, because of intellectual properties (IP) issues, technology consulting companies can't directly reuse developed functionalities with different clients. In this context, requirements reuse technics presents a good way to capitalize on their knowledge while avoiding IP issues. However, the literature review on requirements reuse processes doesn't propose methods to the application of reuse processes with little information about the system's operational context. In this paper, we present a semi-automated requirement reuse and recycle process for ATS R&D. The process helps designers’ copes with the lack of inputs from the clients. Requirements candidates are retrieved from a database using Natural Language Processing and traceability propagation. It is applied to 3 use cases with inputs less than 5 concepts from the client's needs. The results validate its efficiency through number requirements retrieved and the analysis time consumption

Published
2019
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6. Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps

Author

Victor Talpaert, Guillaume Trehard, B Ravi Kiran, Renzo Verastegui, Senthil Yogamani, Alexandre Lepoutre, Sebastian Süss, Luis Roldão, Benat Irastorza, AKKA Technologies, Robotics & Intelligent Transportation Systems (RITS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Spleenlab, Valeo Vision Systems [Galway], Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), and Roldao Jimenez, Luis Guillermo

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Background subtraction, Computer science, business.industry, Pipeline (computing), Computer Vision and Pattern Recognition (cs.CV), [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Point cloud, Computer Science - Computer Vision and Pattern Recognition, Inlier rejection, 02 engineering and technology, Object detection, Prior Maps, 020901 industrial engineering & automation, Lidar, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, 3D obstacles, Artificial intelligence, Cluster analysis, business
Abstract

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator., Comment: Preprint Submission to ECCVW AutoNUE 2018 - v2 author name accent correction

Published
2018
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7. Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

Author

Stanislas Larnier, Nouara Achour, Michel Devy, Mustapha Lakrouf, Équipe Robotique, Action et Perception (LAAS-RAP), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire de Robotique Parallélisme Electroénergétique [Alger] (LRPE), Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB), Centre de devellopement des technologies avancées (cdta), Centre de devellopement des technologies avancées, AKKA Technologies, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger] (USTHB)

Subjects
050210 logistics & transportation, Occupancy grid mapping, Supervisor, Computer science, business.industry, 05 social sciences, Process (computing), Mobile robot, moving objects detection, 02 engineering and technology, Occupancy grid, Range (mathematics), 0502 economics and business, classification and pedestrian detection, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Point (geometry), visual, [INFO]Computer Science [cs], AdaBoost, Artificial intelligence, business, Real-time operating system
Abstract

International audience; Perception is one of the most important tasks for safe navigation of autonomous mobile robots in outdoor environments that are dynamic and unknown. This paper presents a real time system for detection and classification of dynamic objects using a 2D laser range finder (2D LRF) and a moving camera mounted on the mobile robot. An occupancy grid based detection is implemented using 2D LRF and moving objects are iteratively extracted from the global occupancy grid. A visual classification system is developed using HOG features and AdaBoost as learning process. Dynamic objects detection and visual classification results are coupled to point moving camera on the specified target (specified by mission supervisor) and to identify it. Finally, real time experiments results on the mobile robot in two different scenarios are presented.

Published
2017
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8. Energy Optimal Real-Time Navigation System

Author

Tomas Jurik, Rédha Hamouche, Silviu-Iulian Niculescu, Abdellatif Reama, Arben Cela, René Natowicz, Jérôme Julien, ESIEE Paris, Electronique, Systèmes de communication et Microsystèmes (ESYCOM), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris, Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Dynamical Interconnected Systems in COmplex Environments (DISCO), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AKKA Technologies, Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris-Est Marne-la-Vallée (UPEM)-ESIEE Paris

Subjects
050210 logistics & transportation, 0209 industrial biotechnology, Engineering, Energy management, business.industry, Mechanical Engineering, 05 social sciences, Real-time computing, Information quality, 02 engineering and technology, Energy consumption, 7. Clean energy, Computer Science Applications, 020901 industrial engineering & automation, Embedded system, 0502 economics and business, Automotive Engineering, Information system, Information flow (information theory), Enhanced Data Rates for GSM Evolution, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], business, Intelligent transportation system, Energy (signal processing), ComputingMilieux_MISCELLANEOUS
Abstract

The rapid development of Mobile Internet and Smart Devices and advent of a new generation of Intelligent Transportation Systems (ITS) increase information about present driving conditions and make its prediction possible. Real time traffic information systems (TIS) like SYTADIN help in route to destination planning and traffic state prediction. Energy-optimal routing for electric vehicles creates novel algorithmic challenges where the computation complexity and the quality of information on traffic state are the main issues. This complexity is induced by the possible negative values of edge energy as well as the variability of route and vehicle variables which render the standard algorithms unsuitable. In this paper the authors present an Energy Optimal Real Time Navigation System (EORTNS), implemented on Samsung Galaxy Tab, capable of calculating the route to destination based on information flow obtained from SYTADIN. As an application example the authors propose a real time energy management for a Hybrid Electrical Vehicle (HEV) composed of batteries and Super-Capacitors (SC). The EORTNS is not only capable of energy optimal route to destination calculation with respect to traffic state but also operates the On-Board power splitting between batteries and Super-Capacitors.

Published
2014
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