Search Results for author: Benedetto Piccoli

Found 17 papers, 1 papers with code

Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test

no code implementations26 Feb 2024 Kathy Jang, Nathan Lichtlé, Eugene Vinitsky, Adit Shah, Matthew Bunting, Matthew Nice, Benedetto Piccoli, Benjamin Seibold, Daniel B. Work, Maria Laura Delle Monache, Jonathan Sprinkle, Jonathan W. Lee, Alexandre M. Bayen

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles.

Autonomous Driving Reinforcement Learning (RL) +1

So you think you can track?

no code implementations13 Sep 2023 Derek Gloudemans, Gergely Zachár, Yanbing Wang, Junyi Ji, Matt Nice, Matt Bunting, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Maria Laura Delle Monache, Alexandre Bayen, Benjamin Seibold, Daniel B. Work

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4. 2 mile stretch of 8-10 lane interstate highway near Nashville, TN.

Benchmarking Object +1

Car-Following Models: A Multidisciplinary Review

no code implementations14 Apr 2023 Tianya Terry Zhang, Peter J. Jin, Sean T. McQuade, Alexandre Bayen, Ph. D., Benedetto Piccoli

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS).

Imitation Learning reinforcement-learning +2

Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video

no code implementations17 Sep 2022 Tianya T. Zhang Ph. D., Peter J. Jin Ph. D., Han Zhou, Benedetto Piccoli, Ph. D

In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that imposes parity constraints at both pixel and instance levels to generate instance-aware embeddings for vehicle stripe segmentation on STMap.

Imitation Learning

A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles

no code implementations13 May 2022 Nicolas Kardous, Amaury Hayat, Sean T. McQuade, Xiaoqian Gong, Sydney Truong, Tinhinane Mezair, Paige Arnold, Ryan Delorenzo, Alexandre Bayen, Benedetto Piccoli

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.

Autonomous Vehicles

A deep language model to predict metabolic network equilibria

no code implementations7 Dec 2021 François Charton, Amaury Hayat, Sean T. McQuade, Nathaniel J. Merrill, Benedetto Piccoli

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.

Language Modelling Machine Translation +1

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

no code implementations22 Apr 2021 Jonathan W. Lee, George Gunter, Rabie Ramadan, Sulaiman Almatrudi, Paige Arnold, John Aquino, William Barbour, Rahul Bhadani, Joy Carpio, Fang-Chieh Chou, Marsalis Gibson, Xiaoqian Gong, Amaury Hayat, Nour Khoudari, Abdul Rahman Kreidieh, Maya Kumar, Nathan Lichtlé, Sean McQuade, Brian Nguyen, Megan Ross, Sydney Truong, Eugene Vinitsky, Yibo Zhao, Jonathan Sprinkle, Benedetto Piccoli, Alexandre M. Bayen, Daniel B. Work, Benjamin Seibold

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)

no code implementations2 Apr 2021 Saleh Albeaik, Alexandre Bayen, Maria Teresa Chiri, Xiaoqian Gong, Amaury Hayat, Nicolas Kardous, Alexander Keimer, Sean T. McQuade, Benedetto Piccoli, Yiling You

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.

Generalized solutions to bounded-confidence models

no code implementations1 Dec 2020 Benedetto Piccoli, Francesco Rossi

Several models are written as systems of ordinary differential equations with discontinuous right-hand side: this is a direct consequence of restricting interactions to a bounded region with non-vanishing strength at the boundary.

Optimization and Control

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