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Reinforcement learning (RL) can be used to create a tactical decision-makingagent for autonomous driving. However, previous approaches only outputdecisions and do not provide information about the agent's confidence in therecommended actions. This paper investigates how a Bayesian RL technique, basedon an ensemble of neural networks with additional randomized prior functions(RPF), can be used to estimate the uncertainty of decisions in autonomousdriving. A method for classifying whether or not an action should be consideredsafe is also introduced. The performance of the ensemble RPF method isevaluated by training an agent on a highway driving scenario. It is shown thatthe trained agent can estimate the uncertainty of its decisions and indicate anunacceptable level when the agent faces a situation that is far from thetraining distribution. Furthermore, within the training distribution, theensemble RPF agent outperforms a standard Deep Q-Network agent. In this study,the estimated uncertainty is used to choose safe actions in unknown situations.However, the uncertainty information could also be used to identify situationsthat should be added to the training process.

Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation