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Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model.
Autonomous highway driving using deep reinforcement learning
Nageshrao, S., Tseng, E., Filev, D., 2019 · 1904
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Ossen, S., Hoogendoorn, S.P., 2005 · 1934
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Helly, W., 1959 · 1959
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A behavioural car-following model for computer simulation
Gipps, P.G., 1981 · 1981
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Microscopic traffic simulation: the simulation system mission, background and actual state, cec project icarus (v1052), final report, vol. 2, appendix a
Wiedemann, R., Reiter, U., 1992 · 1992
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A model for traffic simulation
Fritzsche, H.T., 1994 · 1994
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Dynamical model of traffic congestion and numerical simulation
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A novel car-following control model combining machine learning and kinematics models for automated vehicles
Yang, D., Zhu, L., Liu, Y., Wu, D., Ran, B., 2018 · 2000
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Full velocity difference model for a car-following theory
Jiang, R., Wu, Q., Zhu, Z., 2001 · 2001
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An analysis of gipps’s car-following model of highway traffic
Wilson, R.E., 2001 · 2001
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Modeling system dynamics with physics-informed neural networks based on lagrangian mechanics
Roehrl, M.A., Runkler, T.A., Brandtstetter, V., Tokic, M., Obermayer, S., 2020 · 2005
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Neural agent car-following models
Panwai, S., Dia, H., 2007 · 2006
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Modeling stochastic microscopic traffic behaviors: a physics regularized gaussian process approach
Yuan, Y., Wang, Q., Yang, X.T., 2020 · 2007
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Optimal velocity functions for car-following models
Batista, M., Twrdy, E., 2010 · 2010
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Non-lane-based full velocity difference car following model
Jin, S., Wang, D., Tao, P., Li, P., 2010 · 2010
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Genetic algorithm: Review and application
Kumar, M., Husain, M., Upreti, N., Gupta, D., 2010 · 2010
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Performance analysis of various activation functions in generalized mlp architectures of neural networks
Karlik, B., Olgac, A.V., 2011 · 2011
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Heterogeneity in car-following behavior: Theory and empirics
Ossen, S., Hoogendoorn, S.P., 2011 · 2011
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Prediction and field validation of traffic oscillation propagation under nonlinear car-following laws
Li, X., Wang, X., Ouyang, Y., 2012 · 2012
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Microscopic calibration and validation of car-following models–a systematic approach
Treiber, M., Kesting, A., 2013 · 2013
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Analysis of asymmetric driving behavior using a self-learning approach
Wei, D., Liu, H., 2013 · 2013
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Dynamics of connected vehicle systems with delayed acceleration feedback
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Alber, M., Tepole, A.B., Cannon, W.R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W.W., Perdikaris, P., Petzold, L., Kuhl, E., 2019 · 2019
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Weight-free multi-objective predictive cruise control of autonomous vehicles in integrated perturbation analysis and sequential quadratic programming optimization framework
He, D., Shi, Y., Song, X., 2019 · 2019
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Stabilizing traffic via autonomous vehicles: A continuum mean field game approach
Huang, K., Di, X., Du, Q., Chen, X., 2019 · 2019
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Is more always better? the impact of vehicular trajectory completeness on car-following model calibration and validation
Sharma, A., Zheng, Z., Bhaskar, A., 2019 · 2019
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Adversarial uncertainty quantification in physics-informed neural networks
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Jin, I.G., Orosz, G., 2014 · 2014
Cited alongside, same era.
A simple nonparametric car-following model driven by field data
He, Z., Zheng, L., Guan, W., 2015 · 2015
Cited alongside, same era.
Tensorflow: learning functions at scale, in: Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, pp. 1–1
Abadi, M., 2016 · 2016
Cited alongside, same era.
Calibrating the wiedemann’s vehicle-following model using mixed vehicle-pair interactions
Durrani, U., Lee, C., Maoh, H., 2016 · 2016
Cited alongside, same era.
A review analysis of optimal velocity models
Lazar, H., Rhoulami, K., Rahmani, D., 2016 · 2016
Cited alongside, same era.
A critical evaluation of the next generation simulation (ngsim) vehicle trajectory dataset
Coifman, B., Li, L., 2017 · 2017
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Theory-guided data science: A new paradigm for scientific discovery from data
Karpatne, A., Atluri, G., et al, 2017 · 2017
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Yang, Y., Perdikaris, P., 2019 · 2019
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Robust local and string stability for a decentralized car following control strategy for connected automated vehicles
Zhou, Y., Ahn, S., 2019 · 2019
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Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability
Zhou, Y., Wang, M., Ahn, S., 2019 · 2019
Later among the works it cites.
Finding multiple solutions of odes with neural networks, in: AAAI-MLPS 2020, CEUR-WS. pp. 1–7
Di Giovanni, M., Sondak, D., Protopapas, P., Brambilla, M., 2020 · 2020
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Physics-informed neural network framework for partial differential equations on 3D surfaces: Time independent problems
Fang, Z., Zhan, J., 2020 · 2020
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Scalable traffic stability analysis in mixed-autonomy using continuum models
Huang, K., Di, X., Du, Q., Chen, X., 2020 · 2020
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Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
Rai, R., Sahu, C.K., 2020 · 2020
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Raissi, M., Yazdani, A., Karniadakis, G.E., 2020 · 2020
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Long-term prediction of lane change maneuver through a multilayer perceptron
Shou, Z., Wang, Z., Han, K., Liu, Y., Tiwari, P., Di, X., 2020 · 2020
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Modeling car-following behaviors and driving styles with generative adversarial imitation learning
Zhou, Y., Fu, R., Wang, C., Zhang, R., 2020 · 2020
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Impact on car following behavior of a forward collision warning system with headway monitoring
Zhu, M., Wang, X., Hu, J., 2020 · 2020
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A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
Di, X., Shi, R., 2021 · 2021
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Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models
Shi, R., Mo, Z., Di, X., 2021a · 2021
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Model-informed machine learning for multi-component t2 relaxometry
Yu, T., Canales-Rodríguez, E.J., Pizzolato, M., Piredda, G.F., Hilbert, T., Fischi-Gomez, E., Weigel, M., Barakovic, M., Cuadra, M.B., Granziera, C., et al., 2021 · 2021
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An lstm-based autonomous driving model using a waymo open dataset
Gu, Z., Li, Z., Di, X., Shi, R., 2020 · 2046
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