no code implementations • 11 Mar 2024 • Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario.
no code implementations • 24 Aug 2023 • Erik Börve, Nikolce Murgovski, Leo Laine
In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other.
no code implementations • 10 Mar 2023 • Mohammad Otoofi, William J. B. Midgley, Leo Laine, Henderson Leon, Laura Justham, James Fleming
It is common to utilise dynamic models to measure the tyre-road friction in real-time.
1 code implementation • 21 May 2021 • Carl-Johan Hoel, Krister Wolff, Leo Laine
The distribution over returns is estimated by learning its quantile function implicitly, which gives the aleatoric uncertainty, whereas an ensemble of agents is trained on bootstrapped data to provide a Bayesian estimation of the epistemic uncertainty.
1 code implementation • 22 Apr 2020 • Carl-Johan Hoel, Krister Wolff, Leo Laine
This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving.
no code implementations • 6 May 2019 • Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff, Leo Laine, Mykel J. Kochenderfer
This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning.
no code implementations • 14 Mar 2018 • Carl-Johan Hoel, Krister Wolff, Leo Laine
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function.