Decision-Making in Autonomous Driving using Reinforcement Learning
Doctoral thesis, 2021
A general decision-making agent, derived from the Deep Q-Network (DQN) algorithm, is proposed. With few modifications, this method can be applied to different driving environments, which is demonstrated for various simulated highway and intersection scenarios. A more sample efficient agent can be obtained by incorporating more domain knowledge, which is explored by combining planning and learning in the form of Monte Carlo tree search and RL. In different highway scenarios, the combined method outperforms using either a planning or a learning-based strategy separately, while requiring an order of magnitude fewer training samples than the DQN method.
A drawback of many learning-based approaches is that they create black-box solutions, which do not indicate the confidence of the agent's decisions. Therefore, the Ensemble Quantile Networks (EQN) method is introduced, which combines distributional RL with an ensemble approach, to provide an estimate of both the aleatoric and the epistemic uncertainty of each decision. The results show that the EQN method can balance risk and time efficiency in different occluded intersection scenarios, while also identifying situations that the agent has not been trained for. Thereby, the agent can avoid making unfounded, potentially dangerous, decisions outside of the training distribution.
Finally, this thesis introduces a neural network architecture that is invariant to permutations of the order in which surrounding vehicles are listed. This architecture improves the sample efficiency of the agent by the factorial of the number of surrounding vehicles.
Monte Carlo tree search
epistemic uncertainty
tactical decision-making
reinforcement learning
neural networks
aleatoric uncertainty
autonomous driving
Author
Carl-Johan E Hoel
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning With Applications in Autonomous Driving
IEEE Transactions on Intelligent Transportation Systems,;Vol. In Press(2023)
Journal article
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
IEEE Intelligent Vehicles Symposium, Proceedings,;(2020)p. 1563-1569
Paper in proceeding
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
IEEE Transactions on Intelligent Vehicles,;Vol. 5(2020)p. 294-305
Journal article
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC,;(2020)
Paper in proceeding
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC,;(2018)p. 2148-2155
Paper in proceeding
An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior
Proceedings of 16th IEEE International Conference On Machine Learning And Applications (ICMLA),;(2017)
Paper in proceeding
The results of the thesis show that the introduced RL-based methods can be used to teach an artificial driver how to behave in different simulated highway and intersection scenarios. The results also show that if the driver is provided with a simple model of the traffic scenario, it can learn a suitable behavior faster. Furthermore, a method that allows the driver to estimate how confident it is about its decisions is introduced. If the artificial driver encounters a situation that it has not seen before, such as a wild animal on the road, the driver can identify that it is uncertain about what to do and instead act in a precautionary way, to minimize the risk of an accident.
Areas of Advance
Transport
Subject Categories
Computer Science
Computer Vision and Robotics (Autonomous Systems)
ISBN
978-91-7905-584-4
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5051
Publisher
Chalmers
Lecture room FB, Fysikgården 4, Chalmers
Opponent: Professor Ville Kyrki, Department of Electrical Engineering and Automation, Aalto University, Finland