Driving in dense traffic with human and autonomous drivers is a challenging task that requires high-level planning and reasoning. Human drivers can achieve this task comfortably, and there has been many efforts to model human driver strategies. These strategies can be used as inspirations for developing autonomous driving algorithms or to create high-fidelity simulators. Reinforcement learning is a common tool to model driver policies, but conventional training of these models can be computationally expensive and time-consuming. To address this issue, in this paper, we propose ``skill-based" hierarchical driving strategies, where motion primitives, i.e. skills, are designed and used as high-level actions. This reduces the training time for applications that require multiple models with varying behavior. Simulation results in a merging scenario demonstrate that the proposed approach yields driver models that achieve higher performance with less training compared to baseline reinforcement learning methods.

翻译：在存在人类和自动驾驶车辆的密集交通环境中驾驶是一项需要高级规划与推理的挑战性任务。人类驾驶员能够舒适地完成该任务，为此已有大量研究致力于建模人类驾驶策略。这些策略可作为开发自动驾驶算法或构建高保真模拟器的灵感来源。强化学习是建模驾驶员策略的常用工具，但传统模型训练需要大量计算资源和时间。为解决此问题，本文提出了一种基于技能的层级化驾驶策略——通过设计运动基元（即技能）并将其作为高层动作，可缩短需要生成多种不同行为模型的应用场景的训练时间。在合流场景下的仿真结果表明，与基线强化学习方法相比，所提方法能以更少的训练代价获得具有更优性能的驾驶员模型。