Reinforcement Learning for Highway Driving Decision-making Using CDR (Curriculum based Domain Randomization)

Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision- making in highways is essential. However, decision-making in highways is a challenging task due to the complexity of traffic situations. Thus, reinforcement learning is an appropriate approach for decision-making in highways. However, a domain gap often arises between the training and testing environments due to differences in them. Therefore, a curriculum-based domain randomization approach is applied, which progressively adjusts the level of random parameters. The RL model is trained using the SUMO simulator and tested in the MORAI simulator, where white noise is added to simulate real-world conditions. Consequently, applying curriculum domain randomization has the lowest collision rate.