Search Results for author: Benjamin Seibold

Found 10 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

Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

no code implementations10 Oct 2023 Nour Khoudari, Sulaiman Almatrudi, Rabie Ramadan, Joy Carpio, Mengsha Yao, Kenneth Butts, Alexandre M. Bayen, Jonathan W. Lee, Benjamin Seibold

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components.

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

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.

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