Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
This work addresses decision-making efficiency for autonomous vehicles, but it appears incremental as it applies existing transfer learning methods to a specific driving scenario.
The paper tackles the problem of real-time decision-making for autonomous vehicles by constructing a transfer deep reinforcement learning framework to transform driving tasks like left turns, right turns, and running straight at unsignalized intersections, with results showing that decision-making strategies for similar tasks are transferable, reducing time consumption and enabling online implementation.
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making pol-icy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce the time consumption and realize online implementation.