Search Results for author:
Found 8 papers, 1 papers with code
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
The upper layer is called Speed Planner, and is a centralized optimal control algorithm.
Traffic smoothing using explicit local controllers
The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving.
HyperTree Proof Search for Neural Theorem Proving
With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy.
Ranked #1 on
Automated Theorem Proving
on Metamath set.mm
(Pass@32 metric)
A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles
The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors.
A deep language model to predict metabolic network equilibria
We show that deep learning models, and especially architectures like the Transformer, originally intended for natural language, can be trained on randomly generated datasets to predict to very high accuracy both the qualitative and quantitative features of metabolic networks.
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems.
Limitations and Improvements of the Intelligent Driver Model (IDM)
First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model.
Learning advanced mathematical computations from examples
Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability.
