The paper surveys EV charging literature through a Planning-Scheduling-Behavior framework and diagnoses a fidelity-tractability trilemma in cross-layer integration.
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7 Pith papers cite this work. Polarity classification is still indexing.
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IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
Clear2Fog generates realistic synthetic fog from clear scenes, enabling mixed-density training that outperforms full fixed-density data and improves real-world performance by 1.67 mAP after learning-rate adjustment.
The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
DAIRE is a lightweight ANN with neuron counts scaled as layer index times number of classes that detects CAN attacks in IoV at 99.96% accuracy and 0.03 ms per sample on public datasets.
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
citing papers explorer
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Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
The paper surveys EV charging literature through a Planning-Scheduling-Behavior framework and diagnoses a fidelity-tractability trilemma in cross-layer integration.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
Clear2Fog generates realistic synthetic fog from clear scenes, enabling mixed-density training that outperforms full fixed-density data and improves real-world performance by 1.67 mAP after learning-rate adjustment.
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Adaptive Bounded-Rationality Modeling of Early-Stage Takeover in Shared-Control Driving
The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
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DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles
DAIRE is a lightweight ANN with neuron counts scaled as layer index times number of classes that detects CAN attacks in IoV at 99.96% accuracy and 0.03 ms per sample on public datasets.
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BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.