13 citations · 32 across the 5 of their papers we have counts for
5 papers
Shared learning of powertrain control policies for vehicle fleets
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco
Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other perform…
Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System
Habtamu Hailemichael, Beshah Ayalew, Lindsey Kerbel +2
Reinforcement learning (RL)-based driver assistance systems seek to improve fuel consumption via continual improvement of powertrain control actions considering experiential data f…
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control
Habtamu Hailemichael, Beshah Ayalew, Lindsey Kerbel +2
Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy eff…
Residual Policy Learning for Powertrain Control
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco +1
Eco-driving strategies have been shown to provide significant reductions in fuel consumption. This paper outlines an active driver assistance approach that uses a residual policy l…
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco +1
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of othe…