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

arXiv: https://arxiv.org/abs/2104.11267 · HTML: https://mlsanity.com/paper/2104.11267/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments](https://mlsanity.com/paper/2302.07453/) (2023)
- [Learning energy-efficient driving behaviors by imitating experts](https://mlsanity.com/paper/2208.12534/) (2022)
- [Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data](https://mlsanity.com/paper/2401.09666/) (2024)

## Abstract (arXiv)

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. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.