313 citations · 619 across the 7 of their papers we have counts for
7 papers
Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control
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. To tackle this proble…
Estimating friction coefficient using generative modelling
Mohammad Otoofi, William J. B. Midgley, Leo Laine +3
It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the envir…
Applying quantum approximate optimization to the heterogeneous vehicle routing problem
David Fitzek, Toheed Ghandriz, Leo Laine +2
Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test thes…
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving
Carl-Johan Hoel, Krister Wolff, Leo Laine
Reinforcement learning (RL) can be used to create a decision-making agent for autonomous driving. However, previous approaches provide only black-box solutions, which do not offer…
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
Carl-Johan Hoel, Krister Wolff, Leo Laine
Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide inf…
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff +2
Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with oth…
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
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. A Deep Q-Network agent was trained in…