Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2024Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data18 Jan 2024
- 2023Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments15 Feb 2023
- 2022Learning energy-efficient driving behaviors by imitating experts28 Jun 2022