A rigorous optimal control formulation for a mixed-autonomy platoon with the Bando-FtL model is shown to have a minimizer and is solved by adjoint gradient descent, with simulations showing large reductions in acceleration and fuel use for one to five autonomous vehicles.
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
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abstract
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.
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math.OC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Optimal Control of ODE Car-Following Models: Applications to Mixed-Autonomy Platoon Control via Coupled Autonomous Vehicles
A rigorous optimal control formulation for a mixed-autonomy platoon with the Bando-FtL model is shown to have a minimizer and is solved by adjoint gradient descent, with simulations showing large reductions in acceleration and fuel use for one to five autonomous vehicles.