234 citations · 479 across the 10 of their papers we have counts for
4 papers · 1 filter
Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning
Yuan Lin, John McPhee, Nasser L. Azad
Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a po…
Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control
Yuan Lin, John McPhee, Nasser L. Azad
This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order system i…
Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning
Yuan Lin, John McPhee, Nasser L. Azad
Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and longitudinal control for high-speed on-ramp merging. The DRL environment state includes the sta…
Adaptive Tube-based Nonlinear MPC for Ecological Autonomous Cruise Control of Plug-in Hybrid Electric Vehicles
Bijan Sakhdari, Nasser L. Azad
This paper proposes an adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of autonomous cruise control (ACC) systems. The proposed method utili…