Abstract-Machine learning techniques have been shown to outperform many rule-based systems for the decision-making of autonomous vehicles. However, applying machine learning is challenging due to the possibility of executing unsafe actions and slow learning rates. We address these issues by presenting a reinforcement learning-based approach, which is combined with formal safety verification to ensure that only safe actions are chosen at any time. We let a deep reinforcement learning (RL) agent learn to drive as close as possible to a desired velocity by executing reasonable lane changes on simulated highways with an arbitrary number of lanes. By making use of a minimal state representation, consisting of only 13 continuous features, and a Deep Q-Network (DQN), we are able to achieve fast learning rates. Our RL agent is able to learn the desired task without causing collisions and outperforms a complex, rule-based agent that we use for benchmarking.
Therefore, reinforcement learning has been combined with formal verification to ensure that only safe actions are executed by the reinforcement learning algorithm REF .
High-level Decision Making for Safe and Reasonable Autonomous Lane Changing using Reinforcement Learning
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Engineering Computer Science