Search Results for author:
Found 7 papers, 2 papers with code
Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
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.
Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control
In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other.
Estimating friction coefficient using generative modelling
It is common to utilise dynamic models to measure the tyre-road friction in real-time.
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving
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.
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
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.
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
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.
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function.
