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Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing research interest in recent decades. In this study, we propose an adaptable personalized car-following framework —– MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning. • Introduced a car-following model based on meta-learning. • Segmented the driving behavior of different drivers. • Validated the effectiveness of our approach using real-world driving data. [ABSTRACT FROM AUTHOR]

A delicate balance between safety, efficiency, and fluidity needs to be carefully maintained in vehicle following, in strict accordance with real-time control imperatives. Achieving efficient vehicle-following operations under safe driving conditions, through smooth behavioral adjustments, presents a significant challenge for data-driven vehicle-following models. In response to this challenge, we have developed a deep neural network based on gated recurrent unit (GRU) with attention mechanism, named AGRUNet model, for artificial intelligence (AI) control of vehicle following behavior. Through training and testing on diverse datasets, the AGRUNet model not only establishes a nonlinear mapping relationship between the following vehicle's acceleration and the speeds of the leading and following vehicles, their distance, and the control strategy of the leading vehicles but also accurately forecasts the future behaviors of following vehicles in complex vehicle-following scenarios in real-time. This capability enables the following vehicle to optimize its behavior based on the current vehicle-following situation and control requirements, thereby improving safety, efficiency, and smoothness. Rigorous simulations of AGRUNet on the Highway Drone(HighD), Next Generation Simulation(NGSIM), Waymo, and Lyft Level 5(Lyft) datasets demonstrate its superior performance in prediction accuracy and vehicle-following control. Compared to the widely adopted, high-performance Long Short-Term Memory (LSTM) model, AGRUNet achieves prediction accuracy gains of approximately 2%, 7%, 22%, and 3% across these datasets. Extensive testing further indicates that AGRUNet significantly reduces collision rates during sudden emergency braking by the leading vehicle, enhancing safety, and improving the efficiency and smoothness of behavior adjustments, all while ensuring vehicle-following safety. • An AGRUNet model is developed to optimize vehicle-following control under complex scenarios. • The model integrates attention mechanism and GRU to capture crucial features for vehicle following. • Extensive evaluations on public driving datasets demonstrate superior performance. • Improving vehicle-following behavior in terms of safety, efficiency, and smoothness. [ABSTRACT FROM AUTHOR]

Platelet-derived growth factor receptor (PDGFR) signaling is involved in proliferation and survival in a wide array of cell types. The role of PDGFR signaling in heart regeneration is still unknown. We find that PDGFR-β signaling decreases in myocardium with age and that conditional activation PDGFR-β in cardiomyocytes promotes heart regeneration. Employing RNA sequencing, we show that the enhancer of zeste homolog 2 (Ezh2) can be upregulated by PDGFR-β signaling in primary cardiomyocytes. Conditional knockout of Ezh2 blocks cardiomyocyte proliferation and H3K27me3 modification during neonatal heart regeneration with Ink4a/Arf upregulation, even in mice with myocyte-specific conditional activation of PDGFR-β. We also show that PDGFR-β controls EZH2 expression via the phosphatidylinositol 3-kinase (PI3K)/p-Akt pathway in cardiomyocytes. Gene therapy with adeno-associated virus serotype 9 (AAV9) encoding activated PDGFR-β enhances adult heart regeneration and systolic function. Our data demonstrate that the PDGFR-β/EZH2 pathway is critical for promoting cardiomyocyte proliferation and heart regeneration, providing a potential target for cardiac repair. • Conditional PDGFR-β activation in cardiomyocytes promotes heart regeneration • EZH2 is required in PDGFR-β-induced cardiomyocyte proliferation • PDGFR-β regulates EZH2 expression via the PI3K/p-Akt pathway • AAV9-mediated PDGFR-β activation improves adult cardiac repair and systolic function Yue et al. reveal that PDGFR-β activation is sufficient to promote cardiomyocyte proliferation and heart regeneration by stimulating EZH2-mediated Ink4a/Arf repression. AAV9-encoding-activated PDGFR-β improves adult myocardial repair and systolic function, providing a potential target to prevent heart failure after cardiac injury. [ABSTRACT FROM AUTHOR]