On the public citation graph

Robust Data-Driven Vehicle State Estimation Under Measurements Uncertainties

https://doi.org/10.2139/ssrn.4856907
0 signed · 37 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.ejcon.2014.04.005openalex-snapshot
Development and numerical validation of a reduced-order two-track car model
inferredcites →https://doi.org/10.1016/j.eswa.2017.11.028openalex-snapshot
A new image classification method using CNN transfer learning and web data augmentation
inferredcites →https://doi.org/10.1016/j.measurement.2021.109541openalex-snapshot
Data-driven vehicle modeling of longitudinal dynamics based on a multibody model and deep neural networks
inferredcites →https://doi.org/10.1016/j.trc.2020.102644openalex-snapshot
A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data
inferredcites →https://doi.org/10.1016/j.trc.2021.103414openalex-snapshot
An improved learning-based LSTM approach for lane change intention prediction subject to imbalanced data
inferredcites →https://doi.org/10.1016/s0001-4575(99)00013-5openalex-snapshot
A collision model for safety evaluation of autonomous intelligent cruise control
inferredcites →https://doi.org/10.1049/cth2.12587openalex-snapshot
Dynamic mass estimation framework for autonomous vehicle system via bidirectional gated recurrent unit
inferredcites →https://doi.org/10.1049/iet-its.2019.0428openalex-snapshot
Time‐aware gated recurrent unit networks for forecasting road surface friction using historical data with missing values
inferredcites →https://doi.org/10.1051/matecconf/201816602001openalex-snapshot
Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network
inferredcites →https://doi.org/10.1061/jtepbs.0000754openalex-snapshot
Adaptive Lane-Departure Prediction Method with Support Vector Machine and Gated Recurrent Unit Models
inferredcites →https://doi.org/10.1080/00423110500385949openalex-snapshot
Dual extended Kalman filter for vehicle state and parameter estimation
inferredcites →https://doi.org/10.1080/00423114.2010.527994openalex-snapshot
Unscented Kalman filter for vehicle state estimation
inferredcites →https://doi.org/10.1080/00423114.2014.920090openalex-snapshot
Comparative study of vehicle tyre–road friction coefficient estimation with a novel cost-effective method
inferredcites →https://doi.org/10.1080/00423114.2019.1638947openalex-snapshot
Modelling longitudinal vehicle dynamics with neural networks
inferredcites →https://doi.org/10.1109/access.2021.3112560openalex-snapshot
Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking
inferredcites →https://doi.org/10.1109/iccma46720.2019.8988688openalex-snapshot
NMPC-based Controller for Autonomous Vehicles Considering Handling Performance
inferredcites →https://doi.org/10.1109/iccwamtip53232.2021.9674096openalex-snapshot
Spatial-Temporal Attentive Motion Planning Network for Autonomous Vehicles
inferredcites →https://doi.org/10.1109/itec53557.2022.9813990openalex-snapshot
Cooperative Adaptive Cruise Control: A Gated Recurrent Unit Approach
inferredcites →https://doi.org/10.1109/iv55152.2023.10186695openalex-snapshot
Robust LSTM-based Vehicle Velocity Observer for Regular and Near-limits Applications
inferredcites →https://doi.org/10.1109/ivs.2017.7995816openalex-snapshot
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
inferredcites →https://doi.org/10.1109/mits.2018.2879224openalex-snapshot
Building an Autonomous Lane Keeping Simulator Using Real-World Data and End-to-End Learning
inferredcites →https://doi.org/10.1109/smc42975.2020.9283293openalex-snapshot
Learning-based Model Predictive Control for Path Tracking Control of Autonomous Vehicle
inferredcites →https://doi.org/10.1109/smc53992.2023.10394125openalex-snapshot
Data-Driven Vehicle Dynamic Model for Autonomous Vehicle Applications
inferredcites →https://doi.org/10.1109/smc53992.2023.10394243openalex-snapshot
A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle
inferredcites →https://doi.org/10.1109/tcst.2021.3130225openalex-snapshot
Neural Network Model Predictive Motion Control Applied to Automated Driving With Unknown Friction
inferredcites →https://doi.org/10.1109/tie.2019.2952807openalex-snapshot
Dynamic State Estimation for the Advanced Brake System of Electric Vehicles by Using Deep Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/tvt.2021.3064287openalex-snapshot
Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning
inferredcites →https://doi.org/10.1126/scirobotics.aaw1975openalex-snapshot
Neural network vehicle models for high-performance automated driving
inferredcites →https://doi.org/10.1162/neco_a_01199openalex-snapshot
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
inferredcites →https://doi.org/10.1177/0037549717709932openalex-snapshot
Object recognition and detection with deep learning for autonomous driving applications
inferredcites →https://doi.org/10.1299/mej.21-00045openalex-snapshot
Forecasting vehicle accelerations using LSTM
inferredcites →https://doi.org/10.3115/v1/w14-4012openalex-snapshot
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
inferredcites →https://doi.org/10.3390/app8122508openalex-snapshot
Estimation of the Lateral Distance between Vehicle and Lanes Using Convolutional Neural Network and Vehicle Dynamics
inferredcites →https://doi.org/10.3390/s22134676openalex-snapshot
An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms
inferredcites →https://doi.org/10.3390/s23020660openalex-snapshot
A CNN-LSTM Car-Following Model Considering Generalization Ability
inferredcites →https://doi.org/10.48550/arxiv.1412.3555openalex-snapshot
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
inferredcites →https://doi.org/10.48550/arxiv.2301.06201openalex-snapshot
Risk-aware Vehicle Motion Planning Using Bayesian LSTM-Based Model Predictive Control
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.