  Decision-making module enables autonomous vehicles to reach appropriate
maneuvers in the complex urban environments, especially the intersection
situations. This work proposes a deep reinforcement learning (DRL) based
left-turn decision-making framework at unsignalized intersection for autonomous
vehicles. The objective of the studied automated vehicle is to make an
efficient and safe left-turn maneuver at a four-way unsignalized intersection.
The exploited DRL methods include deep Q-learning (DQL) and double DQL.
Simulation results indicate that the presented decision-making strategy could
efficaciously reduce the collision rate and improve transport efficiency. This
work also reveals that the constructed left-turn control structure has a great
potential to be applied in real-time.
