Hugging Face Trending Papers

Robostreet Flow: A Lightweight, Ultra-Low-Drag Electric Tractor and Four-Truck Hybrid Convoy Architecture for Minimum-Cost Point-to-Point Freight

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Line-haul trucking costs are dominated by three comparably sized components: energy, driver labor, and equipment. Most efficiency technologies address only one component at a time.

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arXiv Machine Learning
Oct 2

SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks

SHIFT‑Truck is the first high‑fidelity aerodynamics dataset and benchmark specifically for pickup trucks, comprising 1,000 Spalart‑Allmaras delayed detached‑eddy simulations of a reference truck geometry morphed across 17 shape parameters. Each simulation uses a ~100‑million‑cell mesh at a Reynolds number of 1.4×10⁷ and provides time‑averaged surface pressure, wall shear stress, volumetric pressure, and velocity data, verified by grid refinement, repeated runs, and wind‑tunnel measurements. The benchmark defines geometry‑grouped splits and evaluates four neural surrogates—DoMINO, GeoTransolver, AB‑UPT, and SMART—on surface and volume tracks, while also introducing controlled distribution shifts in operating point, surface discretization, and vehicle archetype to test generalization.

By Riddhiman Raut, Yin Yu, Aashwin Anand Mishra, Michael Emory, Thomas Economon, Peter Lyu, Juan J. Alonso
arXiv Machine Learning
Sep 11

EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading

EVTradeMatch is a mobility-aware, multi-objective matching framework that coordinates peer-to-peer energy trading between electric vehicles (EVs). It uses a prediction-guided score for charging-node suitability and formulates the matching problem as a mixed-integer linear program, solved via a tailored NSGA-II algorithm. Experiments show significant gains in transferred energy, charging-node suitability, and matching coverage compared to existing proximity- and auction-based methods.

By Md. Mahfujur Rahman, Alistair Barros, Raja Jurdak, Darshika Koggalahewa