3 research outputs found
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
Decision-making strategy for autonomous vehicles de-scribes a sequence of
driving maneuvers to achieve a certain navigational mission. This paper
utilizes the deep reinforcement learning (DRL) method to address the
continuous-horizon decision-making problem on the highway. First, the vehicle
kinematics and driving scenario on the freeway are introduced. The running
objective of the ego automated vehicle is to execute an efficient and smooth
policy without collision. Then, the particular algorithm named proximal policy
optimization (PPO)-enhanced DRL is illustrated. To overcome the challenges in
tardy training efficiency and sample inefficiency, this applied algorithm could
realize high learning efficiency and excellent control performance. Finally,
the PPO-DRL-based decision-making strategy is estimated from multiple
perspectives, including the optimality, learning efficiency, and adaptability.
Its potential for online application is discussed by applying it to similar
driving scenarios.Comment: 9 pages, 10 figure