Residual Koopman MPC for Enhanced Vehicle Dynamics
2025
[KoopmanMPC2025]
Bibliographie
Bibliographie – RS3 / Telemachus
227 références — généré le 03/04/2026 18:56:12
ADAS & conduite autonome 20
Publications traitant des systèmes ADAS, de la conduite automatisée et des architectures logicielles associées.
CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving
2024
[CoopScenes2024]
Génération de trajectoires locales temps réel pour un véhicule autonome dans un environnement dynamique et coopératif
2024
[Vigne2024-LocalTrajCoop]
Thèse (UGA 2024) sur la localisation coopérative et la trajectographie locale contrainte par la carte pour véhicules intelligents. Apporte des méthodes map-aware et coopératives utiles pour la robustesse GNSS/INS en urbain (papier GNSS/INS robuste en milieu urbain : contraintes véhicule, inertiel et SLAM) et l’alignement avec RoadGeometry (artefact Telemachus RoadGeometry Extension (RFC-0015)).
Optimizing Energy-Efficient Braking Trajectories
2024
[Alvarez2024-OptimizingEnergy-Eff]
Scénarios de conduite pour la démonstration de sécurité des systèmes automatisés : de la génération à la sélection
2024
[DGITM2024-ScenarioSelection]
Rapport stratégique DGITM sur la sélection et la génération de scénarios critiques (SOTIF, ODD, combinatoire) pour la validation sécurité des véhicules autonomes. Sert d’appui à l’argumentaire RS3 comme injecteur de scénarios reproductibles et à l’intégration BridgeGenScenarioGeneration2023.
BridgeGen: Ontology-Guided Safety-Critical Scenario Generation for Autonomous Driving Validation
2023
[BridgeGenScenarioGeneration2023]
Choose Your Simulator Wisely: A Review on Open-Source Simulators for Autonomous Driving
2023
[ADSPlatforms2025-OpenSourceReview]
Panorama des simulateurs open-source pour la conduite autonome (CARLA, Autoware, Apollo...). Sert de base à l'argumentaire de positionnement RS3 dans billet Plateformes open-source pour la conduite autonome : où se positionne RoadSimulator3 ? : RS3 = simulateur inertiel 10 Hz sans GPU, orienté risque conducteur et énergie, pas juste perception 3D. Utile pour (diffusion recherche).
Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models
2023
[FuzzyCarFollowingCalibration2023]
Wireless Data Transfer Needs for Trip Planning with Reinforcement Learning
2023
[TripPlanningRL2023]
Deep Learning for Radar Data Exploitation of Autonomous Vehicle
2022
[Ouaknine2022-RadarDeepLearning]
OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles
2021
[OODIDA2021]
Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion
2020
[RoundaboutNavigation2020]
Data-Driven Intersection Management Solutions for Mixed Traffic of Human-Driven and Connected and Automated Vehicles
2020
[Bashiri2020-IntersectionManagement]
Mixed Traffic of Human-Driven and Connected and Automated Vehicles
2020
[IntersectionManagement2020]
Real-Time Object Tracking via Adaptive Correlation Filters
2020
[Du2020-RealTimeCorrelationTracking]
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
2020
[SociallyCompatibleBehavior2020]
Survey of Autonomous Driving
2020
[Yurtsever2020-SurveyofAutonomousDr]
Data-driven Identification of Vehicle Dynamics Using Koopman Operator
2019
[KoopmanIdentification2019]
RMT06-016 – Annexes Vol. 2 : Référentiels techniques et protocoles d’essais pour la validation des systèmes automatisés
2016
[RMT062016-AnnexesVol2]
Annexes techniques du rapport RMT06 sur la méthodologie d’essais véhicules et les résultats de référence pour la validation des modèles de simulation. Sert de base pour la calibration et la comparaison des pipelines RS3 avec les essais terrain et la validation industrielle.
Optimal Strategies for the Control of Autonomous Vehicles
2015
[OptimalControl2015]
Détection d’événements & risque 47
Travaux sur la détection d’événements, le risque conducteur et l’analyse des signaux inertiels.
Assessing Driving Risk Through Unsupervised Detection of Anomalies in Telematics Time Series Data
2025
[DrivingRiskAnomalyTelematics2025]
Détection des anomalies via les capteurs des smartphones dans le domaine de la santé
2025
[Krichen2025-DTectionDes]
Extracting Insights from Large-Scale Connected Vehicle Data for Safety and Fleet Analytics
2025
[DrivingInsights2025]
Identification and Estimation of Causal Effects in High-Frequency Event Studies
2025
[Casini2025-IdentificationEstimationCausal]
Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data
2024
[TrafficIncidents2024]
Detection of Road Anomalies from Simulated IMU
2024
[Zhang2024-DetectionofRoadAnoma]
GNSS Multipath Error Detection for Ground Vehicles using Camera Images
2024
[multipathGNSSCamera2024]
History-Based Road Traffic Anomaly Detection Using Deep Learning
2024
[Unknown2024-HistoryRoadTraffic]
Leveraging Connected Vehicle Data for Near-Crash Detection and Analysis in Urban Environments
2024
[NearCrash2024]
Leveraging Connected Vehicle Data for Near-Crash Detection and Analysis in Urban Environments
2024
[Li2024-LeveragingConnectedVehicle]
Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation
2024
[Nguyen2024-RoadAnomalyDetection]
Signal processing methods for accurate event detection: In measuring haptic response
2024
[Juntunen2024-SignalProcessingMethods]
Driving_event_recognition_using_machine_learning_a
2023
[DrivingEventRecognition2023-ML]
Fuel Consumption Estimation via Inertial Data
2023
[Various2023-FuelConsumptionEstim]
In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment
2023
[OlderDriverMCI2023]
Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data
2023
[CrashRiskHurricane2023]
Road Defect Detection Using IMU and CNN
2023
[Udah2023-RoadDefectDetectionU]
Speeding Evaluation from Telematics Data
2023
[Zhou2023-SpeedingEvaluationfr]
Syed - Predicting Real-time Crash Risks during Hurricane
2023
[Syed2023-CrashRiskHurricane]
Unsupervised Driving Event Discovery Based on Vehicle CAN-data
2023
[CANEvents2023]
Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding
2023
[SpeedingEffects2023]
inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data
2023
[CrashLikelihood2023]
Car-Following Driver Model
2022
[Zhao2022-Car-FollowingDriverM]
Driver Lane-Change Behavior: Safety–Efficiency Tradeoffs and Risk Characterization from Trajectory Data
2022
[LaneChangeRiskBehavior2022]
Driver Model for Simulation and Safety
2022
[Wu2022-DriverModelforSimula]
Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review
2022
[FrequencyEventDetection2022]
GNSS Spoofing Detection via IMU
2022
[Chen2022-GNSSSpoofingDetectio]
In-Vehicle Smartphone Sensing for Driving Behaviour Analysis (Part I)
2022
[Cojocaru2022-SmartphoneTelematics]
In-Vehicle Smartphone Sensing for Driving Behaviour Analysis (Part II)
2022
[Cojocaru2022-SmartphoneTelematicsPartII]
A Low-Cost Approach to Identify Hazard Curvature for Local Road Networks Using Open-Source Data
2021
[Hu2021-HazardCurvatureOpenData]
Smartphone + Deep Learning : détection d’événements de conduite
2021
[Guo2021-SmartphoneEventDetection]
Sound Event Detection by Pseudo-Labeling in Weakly Labeled Dataset
2021
[Park2021-SoundEventDetection]
IMU-based Driving Behavior Classification
2020
[Broumandan2020-IMU-basedDrivingBeha]
Pothole Detection Using Smartphone IMU
2020
[Hwang2020-PotholeDetectionUsin]
Driving Behavior Analysis Using IMU
2019
[Ahmed2019-DrivingBehaviorAnaly]
Road Surface Condition Detection Using IMU
2019
[He2019-RoadSurfaceCondition]
Automatic Unusual Driving Event Identification for Dependable Self-Driving
2018
[Li2018-AutomaticUnusualDriving]
Driver Modeling for Vehicle Simulation
2018
[Robert2018-DriverModelingforVeh]
Extracting Useful Information from Connected Vehicle Data: Driving Volatility Measures
2018
[DrivingVolatility2018]
Mobile IMUs Reveal Driver's Identity From Vehicle Turns
2017
[DriverIdentity2017]
Road Geometry-Based Risk Estimation Model for Curved Roads
2017
[CurvatureRiskModel-ITS]
Smartphone-Based Outlier Detection: A Complex Event Processing Approach
2017
[Vasconcelos2017-SmartphoneOutlierDetection]
Driver Behavior Analysis from GPS Data
2016
[Zhu2016-DriverBehaviorAnalys]
Road Curvature and Risk Estimation
2016
[Karaduman2016-RoadCurvatureRisk]
Using Data Analytics to Detect Anomalous States in Vehicles
2015
[AnomalousStates2015]
telematics-Data-Quality-is-King pdf
2015
[DataQualityKing2015-UBI]
Driving Maneuver Detection System Based on GPS Data
2013
[Phondeenana2013-GPSManeuverDetection]
Fusion GNSS/IMU & multi-capteurs 104
Références clés pour la fusion GNSS/INS, les filtres bayésiens et la navigation robuste dans des environnements dégradés.
A Comprehensive Review on Traffic Datasets and Simulators for
2025
[Unknown2025-ComprehensiveReviewTraffic]
An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU
2025
[Xiao2025-InertialSequenceLearning]
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation
2025
[Mafi2025-ConsensusMMKF]
PDF Pourquoi la fusion GNSS–IMU reste un défi en milieu urbain Fusion GNSS/IMU robuste : le filtre à consensus MMKF IMUSim : simuler les capteurs inertiels pour mieux comprendre la fusion GNSS/IMU Consensus MMKF vs. Kalman classique en GNSS dégradé Filtrage invariant et fusion vision-inertielle : leçons pour les environnements GNSS-denied Fusion hybride et robustesse : le filtre de Kalman multi-modèles à consensus (Mafi, 2025) De la simulation à la recherche doctorale : RS3 comme démonstrateur scientifique De la donnée simulée à la donnée validée : comment RS3 et Telemachus préparent la science ouverte de la mobilité
Article pivot introduisant le filtre MMKF à consensus pour la fusion GNSS/IMU robuste.
Incorporating GNSS Information with Lidar–Inertial Odometry for Accurate Land-Vehicle Localization
2025
[LandVehicleLocalization2025]
Joint Optimization-based Targetless Extrinsic Calibration for Multiple LiDARs and GNSS-Aided INS
2025
[ExtrinsicCalibration2025]
Tram Positioning with Map-Enabled GNSS Data Reconciliation
2025
[TramGNSSMapReconciliation2025]
Zhu et al. - 2020 - MIMUOdometer Fusion with State Constraints for Ve
2025
[Zhu2020-MIMUOdometerFusion2]
An Airborne and Gimbal Mounted IMU Signal Simulator Considering Flight Dynamics Model
2024
[Kazemi2024-AirborneGimbalMounted]
BEVRender: Vision-based Cross-view Vehicle Registration in GNSS-denied Environments
2024
[bevrenderGNSSDenied2024]
CVVLSNet: Vehicle Location and Speed Estimation Using Partial Connected Vehicle Trajectory Data
2024
[CVVLSNet2024]
Duan - 2024 - Sensor and sensor fusion technology in autonomous
2024
[Duan2024-SensorFusionAV]
GPS-IMU Sensor Fusion Techniques
2024
[Various2024-GPS-IMUSensorFusionT]
GPS–IMU Sensor Fusion for Reliable Autonomous Vehicles
2024
[Alaba2024-GPSIMUFusion]
Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy
2024
[Brunson2024-InnovativeModelingImu]
Invariant filtering for wheeled vehicle localization with unknown wheel radius and GNSS lever arm
2024
[InvariantFiltering2024]
Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation
2024
[RoadNetworkEstimation2024]
Mahajan et al. - 2024 - Quantifying the Sim2real Gap for GPS and IMU Senso
2024
[Mahajan2024-Sim2realGPSIMU]
Opra et al. - 2024 - Leveraging GNSS and Onboard Visual Data from Consu
2024
[Opra2024-GNSSVisualConsumer]
Physics-Guided Multi-Modal Learning for Robust Vehicle Localization
2024
[PhysicsGuidedMultiModal2024]
Qiu et al. - 2024 - AirIMU Learning Uncertainty Propagation for Inert
2024
[Qiu2024-AirIMU]
A Comprehensive Review of GNSS/INS Integration
2023
[Liu2023-GNSSINSReview]
PDF Pourquoi la fusion GNSS–IMU reste un défi en milieu urbain Comment simuler les pertes GNSS pour valider vos algorithmes de navigation ? De la donnée simulée à la donnée ouverte : itinéraire d’un standard mobilité Fusion GNSS/IMU robuste : le filtre à consensus MMKF IMUSim : simuler les capteurs inertiels pour mieux comprendre la fusion GNSS/IMU Quand la physique rencontre les données : vers une fusion inertielle augmentée (RS3 × Telemachus) De la donnée simulée à la donnée validée : comment RS3 et Telemachus préparent la science ouverte de la mobilité
Revue GNSS/INS de référence couvrant les approches EKF, UKF, PF, et fusions hybrides.
A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicles
2023
[Boguspayev2023-GNSSINSReview]
Advanced Vehicle Localization Framework based on Multi-Sensor Integration
2023
[ASTESJ2023-MultiSensorLocalization]
Article ASTESJ présentant une architecture de localisation intégrée GNSS/IMU/odomètre validée terrain. Sert d’étude de cas et de base comparative pour papier Hybrid Kalman Filtering for Robust GNSS/IMU Fusion — Urban and Open Environments (schémas Kalman hybrides) et papier GNSS/INS robuste en milieu urbain : contraintes véhicule, inertiel et SLAM (robustesse en urbain).
Efficient Real-Time Road Curvature Estimation: Visual–Inertial Approach
2023
[Alrazouk2023-VisualInertialCurvature]
LiDAR–Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion
2023
[LiDARSLAM2023]
Low-Cost GNSS/INS Integration and Sensor Error Compensation for Vehicle Navigation
2023
[JSEN2023-GNSSINSLowCost]
Vehicle Fuel Consumption Virtual Sensing from GNSS and IMU Measurements
2023
[Cellina2023-VirtualSensing]
Zhang et al. - 2023 - Autonomous Vehicles as a Sensor Simulating Data C
2023
[Zhang2023-AVSensorSimulation]
Carrier-phase and IMU based GNSS Spoofing Detection for Ground Vehicles
2022
[GNSSTamper2022]
Determination of Turning Radius and Lateral Acceleration of Vehicle by GNSS/INS Sensor
2022
[Jagelk2022-DeterminationTurningRadius]
Leçon de navigation inertielle — Farrell
2022
[Farrell2022-InertialNavNotes]
Model-based vs Data-driven Estimation of Vehicle Sideslip Angle and Benefits of Tyre Force Measurements
2022
[Bertipaglia2022-SideslipEstimation]
Sideslip Angle and Tyre Force Estimation
2022
[Li2022-SideslipAngleandTyre]
(will be inserted by the editor)
2021
[Unknown2021-WillBeInserted]
Beyond Boundaries: Enhancing Vehicle Egomotion Estimation through IMU-RADAR Tight-Coupling
2021
[Harbers2021-IMURADARTightCoupling]
Chen et Eng - DESIGN AND IMPLEMENTATION OF AN IMU CALIBRATION PL
2021
[Chen2021-IMUCalibPlatform]
Design and Implementation of an IMU Calibration Platform
2021
[Chen2021-LowCostCalibPlatform]
Improved GPS/IMU Loosely Coupled Integration Scheme
2021
[Nagui2021-GPSIMULooselyCoupled]
Présentation du GPSIMU Toolbox open-source pour l’intégration lâche GNSS/IMU (MATLAB/Octave). Sert de référence pédagogique et de base comparative pour la validation des pipelines RS3 et la reproductibilité scientifique ().
Qingqing et al. - 2020 - Multi Sensor Fusion for Navigation and Mapping in
2021
[Qingqing2020-MultiSensorNav]
RailLoMer: Rail Vehicle Localization and Mapping with LiDAR-IMU-Odometer-GNSS Data Fusion
2021
[RailLoMer2021]
A Multi-Core Object Detection Coprocessor for Multi-Sensor Systems
2020
[Xu2020-MultiCoreObjectDetection]
Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration
2020
[Zhang2020-AFISR]
An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar
2020
[Ivanov2020-FMCWVectorModel]
An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar
2020
[Ivanov2020-ElaboratedSignalModel]
An Evaluation of Smartphone Sensor Accuracy and Precision for Low-Cost Applications
2020
[Sensors2020-SmartphoneSensorAccuracy]
Computationally Efficient Cooperative Dynamic Ranging for Multi-Vehicle GNSS/INS Systems
2020
[Kim2020-CooperativeDynamicRanging]
Dynamic IMU Calibration Methods
2020
[Chen2020-DynamicIMUCalibratio]
Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter
2020
[Du2020-GGIW-MD-PMBM]
Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration
2020
[Pop2020-HybridSolutionCombining]
Hybrid Solution Combining Kalman Filtering with Tangent Vectors and Map Matching for Vehicle Localization
2020
[Pop2020-HybridKalmanTangentMapMatching]
Leveraging Uncertainties in Softmax Decision-Making for Deep Learning-Based Classification
2020
[Cho2020-SoftmaxUncertainty]
LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion
2020
[Lastname2020-LorawanGeoTracking]
MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage
2020
[Zhu2020-MIMUOdometerFusion]
Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement — A Review
2020
[Han2020-MEMSRandomErrorReview]
Revue de référence sur les sources d’erreur aléatoires des IMU MEMS, leurs modèles statistiques (bruit blanc, biais, random walk) et leur impact sur la fusion GNSS/IMU. Sert de socle pour la modélisation du bruit inertiel dans RS3 et la robustesse des pipelines de fusion.
Stochastic Modeling of MEMS IMU Sensors
2020
[Fanelli2020-StochasticModelingof]
A Robust Multi-Sensor Data Fusion Clustering Algorithm
2019
[Fan2019-RobustFusionClustering]
Author Instructions for Extended Abstract
2018
[Khattak2018-AuthorInstructionsExtended]
Road Grade Estimation Based on the Curvature of the Road
2018
[RoadGradeFromCurvature]
Chen et al. - 2017 - Mobile IMUs Reveal Driver's Identity From Vehicle Turns
2017
[Chen2017-IMUDriverIdentity]
Development of GPS/INS Integration Module using Kalman Filter
2017
[Yadav2017-GPSINSKalman]
Integrating Satellite and Inertial Navigation-Conventional and New Fusion Approaches
2017
[Wagner2017-IntegratingSatelliteInertial]
MEMS IMU stochastic error modelling
2017
[Hemerly2017-MemsImuStochastic]
Mobile IMUs Reveal Driver’s Identity From Vehicle Turns
2017
[Unknown2017-MobileImusReveal]
Unmanned Ground Vehicle Positioning System by GPS/Dead-Reckoning/IMU Sensor Fusion
2017
[Zhang2017-GPSDRIMUFusion]
ACME 2016 – Road Curvature and Geometry Estimation
2016
[Drosescu2016-ACME]
IMU-Based Smartphone-to-Vehicle Positioning
2016
[Wahlstrom2016-SmartphoneToVehiclePositioning]
A Robust and Easy to Implement Method for IMU Calibration without External Equipments
2014
[Tedaldi2014-IMUCalibNoRig]
A Robust and Easy to Implement Method for IMU Calibration without External Equipments
2014
[Tedaldi2014-RobustEasyImplement]
IEEE TRANS. ON INSTRUM. MEAS.
2014
[Unknown2014-IeeeTransInstrum]
Inverse Perspective Mapping based Road Curvature Estimation
2014
[Seo2014-IPM-Curvature]
Kalman Filtering: Theory and Practice Using MATLAB
2014
[Grewal2014-KalmanFiltering]
Accurate Calibration of MEMS IMU Sensors without External Equipment
2013
[Tedaldi2013-IMUCalibrationNoRig]
Full Auto-Calibration of a Smartphone on Board a Vehicle
2013
[Almazan2013-Autocalibration]
Low-Cost Inertial Navigation System with High Integrity and Reliability for Land Vehicles
2013
[Saadeddin2013-LowCostINS]
Principles of GNSS, Inertial, and Multisensor Navigation Systems
2013
[Groves2013-PrinciplesofGNSS,Ine]
Road Curvature Estimation for Vehicle Lane Departure Detection Using a Robust Takagi–Sugeno Fuzzy Observer
2013
[Dahmani2013-TSFuzzyCurvature]
Optimal Estimation of Dynamic Systems
2012
[Crassidis2012-OptimalEstimationofD]
Estimation of Road Centerline Curvature from Raw GPS Data
2011
[Lipar2011-GPS-Curvature]
Allan Variance and Noise Processes in MEMS Gyroscopes
2010
[GyroNoise2010-AllanVarianceMEMS]
Predictive Collision Sensing with Road Curvature Estimation and Fusion (Patent US 7,522,091)
2009
[US7522091-CurvatureFusion]
Aided Navigation: GPS with High Rate Sensors
2008
[Farrell2008-AidedNavigation]
Methods for in-field user calibration of an inertial measurement unit without external equipment
2008
[Fong2008-FieldUseIMU]
Recent Progress Towards Developing an Insect-Inspired
2008
[Unknown2008-RecentProgressTowards]
Yet another IMU simulator: validation and applications
2008
[Unknown2008-YetAnotherImu]
Global Navigation Satellite Systems, Inertial Navigation, and Integration
2007
[Grewal2007-GlobalNavigationSate]
Stochastic Modelling for GNSS
2007
[Teunissen2007-StochasticGNSS]
GNSS / MEMS IMU Calibration and Integration Methods for Low-Cost Navigation Systems
2006
[Syed2006-MultiPositionCalibration]
IMU Errors and Their Effect on Navigation
2006
[El-Sheimy2006-IMUErrorsandTheirEff]
Integrated GPS/IMU Navigation System with Improved Error Compensation
2006
[FPGI2006-GPSIMUNavigation]
A Novel Data Fusion Approach in an Integrated GPS/INS System
2005
[Asadian2005-GPSINSFusion]
A Low-Cost Navigation System for Land Vehicle Applications
2004
[Grejner2004-LowCostLandNavigation]
Kalman Filter for Beginners with MATLAB Examples
2003
[Zarchan2003-KalmanFilterBeginners]
Sensor Error Modeling for MEMS-IMU
2003
[Meng2003-SensorErrorModelingf]
Estimation with Applications to Tracking and Navigation
2001
[Bar-Shalom2001-EstimationwithApplic]
Integrating Satellite and Inertial Navigation – Conventional and New Fusion Approaches
2001
[Wagner2001-GPSINSConventionalFusion]
Stochastic Road Shape Estimation for Planning and Road Following
2001
[Southall2001-StochasticRoadShape]
GPS 2000: Position-Velocity Aiding of a Mitel ORION Receiver for Sounding-Rocket Tracking
2000
[Montenbruck2000-GpsPositionVelocity]
Integrated INS/GPS Navigation System
2000
[Titterton2000-IntegratedINSGPS]
A Wavelet Tour of Signal Processing
1999
[Mallat1999-AWaveletTourofSignal]
Global Positioning System: Theory and Applications
1998
[Farrell1998-GlobalPositioningSys]
Procedure for in-use calibration of triaxial accelerometers in medical applications
1998
[Lotters1998-InUseAccelCalib]
Practical Issues in the Realization of Robust Geodetic Datum Systems
1997
[Baarda1997-RobustDatumSystems]
Fundamentals of Statistical Signal Processing
1993
[Kay1993-FundamentalsofStatis]
An Elementary Survey of Momentum Methods
1970
[Polyak1970-MomentumMethods]
Données de mobilité & télématique 28
Articles qui structurent la réflexion sur les données de mobilité, la télématique et les usages industriels.
Comprendre les capteurs inertiels low-cost pour la navigation mobile
2025
[IMULowCost2025-FrenchOverview]
Eco-driving with Road Curvature Information: Benchmark and Methods
2025
[Heuts2025-Energy-Curvature]
Inertial Sequence Learning for Vehicle Speed Estimation via Smartphone IMU
2025
[SmartphoneIMUSpeed2025]
Introduction aux ondelettes (analyse temps–fréquence) — Mallat
2025
[Mallat2025-WaveletsPrimer]
Reconnaissance de modes de transport via capteurs smartphone
2025
[TransportModeSmartphone2025]
Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data.
2024
[TRR2024-AdvanceRealTime]
GNSS Measurement-Based Context Recognition for Vehicle Navigation using GRU
2024
[GNSSContextGRU2024]
Perception inertielle + IA pour cartographie haute précision
2023
[Harbers2023-MapGuidedInertial]
Reconstructing Transit Vehicle Trajectory Using High-Resolution GPS Data
2023
[Huang2023-TransitTrajectoryReconstruction]
Determination of Turning Radius and Lateral Acceleration Using Vehicle Telematics Data
2022
[Jagelcak2022-TelematicsTurningRadius]
Fuel-Efficient Driver Assistance via Speed Profile Prediction and Eco-driving Policy Learning
2022
[EcoDrivingFuelPredict2022]
Collection and Use of Uncertain Data in a Mobile GNSS Context
2021
[Stolsmark2021-UncertainMobileGNSS]
Lane-Level Map Matching based on HMM
2021
[Hansson2021-LaneLevelMapMatching]
Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles
2021
[Yu2021-LaneLevelHDMap]
Telematics in Car Fleet Management
2021
[Krol2021-TelematicsFleetManagement]
A Unified Fourth-Order Tensor-Based Smart Community System
2020
[Liu2020-TensorSmartCommunity]
Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA
2020
[ElBoudani2020-DELTA-5GIndoor]
LoRaWAN Geo-Tracking Using Map Matching and Compass Heading
2020
[Podevijn2020-LoRaWANGeoTracking]
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach
2018
[CompressiveCVRecovery2018]
Efficient Collection of Connected Vehicle Data with Precision Guarantees
2018
[CompressiveCVData2018]
Trajectory Clustering for Urban Mobility
2015
[Liu2015-TrajectoryClustering]
Traffic Flow Dynamics
2013
[Treiber2013-TrafficFlowDynamics]
Map Matching with Hidden Markov Model on Sampled Road Network
2012
[Raymond2012-HMMMapMatchingSampled]
Real-Time Lane-Level Road Map Generation Using Mobile Laser Scanning
2012
[Yu2012-LaneLevelHDMap]
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data
2011
[Hunter2011-PathInferenceFilter]
Hidden Markov Map Matching Through Noise and Sparseness
2009
[Newson2009-HMMMapMatching]
Article fondateur sur le map-matching HMM pour la reconstruction de trajectoires GPS bruitées et échantillonnées à basse fréquence. Sert de référence pour l’alignement RS3/Telemachus avec les méthodes de cartographie et la validation de la qualité trajectoire.
Hidden Markov map matching through noise and sparseness
2009
[Newson2009-HiddenMarkovMap]
Intelligent Agents in Traffic and Transportation
2008
[Kesting2008-IntelligentAgentsinT]
Simulation & véhicules autonomes 28
Travaux autour de la simulation de véhicules, des bancs d’essai virtuels et des environnements de conduite autonome.
Génération réaliste des profils gyroscopiques à partir d’événements
2025
[GyroProfiles2025-EventSynthesis]
Infrastructure-to-Vehicle View Transformation with Gaussian Splatting for Autonomous Driving Data Generation
2025
[I2VGS2025]
Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles
2025
[Hallyburton2025-TrustedDataFusion]
UAV Data-Driven Modeling with Integrated 9-Axis IMU-GPS Fusion
2025
[UAVSequenceFusion2025]
Angle Robustness UAV Navigation in GNSS-Denied Scenarios
2024
[AngleRobustnessUAV2024]
Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
2024
[NAVSIM2024]
Pikner et al. - 2024 - Autonomous Driving Validation and Verification Usi
2024
[Pikner2024-ADValidationDigitalTwin]
Robust Object Detection for Autonomous Driving Based on Semi-supervised Learning
2024
[Huilin2024-RobustObjectDetection]
Survey of Autonomous Driving Datasets
2024
[Liu2024-SurveyofAutonomousDr]
AlSaqabi et Krishnamachari - 2022 - Trip Planning for Autonomous Vehicles with Wireles
2023
[AlSaqabi2023-TripPlanningAV]
Autonomous Vehicles as a Sensor: Simulating Data Collection Process
2023
[AVSensor2023]
Sivashangaran et al. - 2023 - AutoVRL A High Fidelity Autonomous Ground Vehicle
2023
[Sivashangaran2023-AutoVRL]
Vehicle Dynamics Identification
2023
[Dani2023-VehicleDynamicsIdent]
City-scale synthetic individual-level vehicle trip data
2022
[CitySyntheticTrips2022]
Data Generation Using Simulation Technology to Improve Perception Mechanism of Autonomous Vehicles
2022
[Cao2022-SimulationPerception]
Mécanique du véhicule et modélisation dynamique — Cours MECA0525
2022
[MECA0525-Dynamics]
Cours structurant (cinématique véhicule, dérive latérale, forces pneus, modèle bicycle) utilisé comme socle physique dans papier GNSS/INS robuste en milieu urbain : contraintes véhicule, inertiel et SLAM (β, contraintes véhicule), papier Virtual Sensing of Energy Consumption from GNSS/IMU Signals (rendement énergétique pente/courbure) et (lecture risque conducteur via courbure/forces latérales).
SIMU-IMU: Synthetic Inertial Dataset for Algorithm Evaluation
2022
[Trung2022-SIMUIMU]
Ali et al. - 2020 - Autonomous Road Roundabout Detection and Navigatio
2020
[Ali2020-RoundaboutDetection]
Simulation de véhicule basée physique (freinage, moteur, braquage)
2020
[Palanisamy2020-PhysicsBasedSim]
Vehicle Simulation for Safety Testing
2019
[Smith2019-VehicleSimulationfor]
Realistic Trajectory Generation
2018
[Zhao2018-RealisticTrajectoryG]
Geometric Tools for Computer Graphics and Robotics
2017
[Vandewalle2017-GeometricToolsforCom]
IMU Reveal : détection de l’état de la route par accéléromètre
2017
[Chen2017-IMURevealRoadState]
Automotive Handbook
2015
[Bosch2015-AutomotiveHandbook]
IMUSim: A Simulation Environment for Inertial Sensing Algorithm Design and Evaluation
2011
[Young2011-IMUSim]
IMUSim définit la base de la simulation inertielle (accéléromètres, gyroscopes, bruit Allan) utilisée ensuite dans RS3. Sert à démontrer la compétence C4 (pipeline simulation inertielle) et C3/C1 côté VAE — via les billets IMUSim : simuler les capteurs inertiels pour mieux comprendre la fusion GNSS/IMU/IMUSim : introduction rapide et outils de base déjà publiés et la validation RS3 → Telemachus.
Vehicle Dynamics and Control
2011
[Rajamani2011-VehicleDynamicsandCo]
Statistical Models in Engineering
2008
[Hald2008-StatModelsEngineering]
Theory of Ground Vehicles
2008
[Wong2008-TheoryofGroundVehicl]