Autonomous driving is an emerging technology that has advanced rapidly over
the last decade. Modern transportation is expected to benefit greatly from a
wise decision-making framework of autonomous vehicles, including the
improvement of mobility and the minimization of risks and travel time. However,
existing methods either ignore the complexity of environments only fitting
straight roads, or ignore the impact on surrounding vehicles during
optimization phases, leading to weak environmental adaptability and incomplete
optimization objectives. To address these limitations, we propose a
parameterized decision-making framework with multi-modal perception based on
deep reinforcement learning, called AUTO. We conduct a comprehensive perception
to capture the state features of various traffic participants around the
autonomous vehicle, based on which we design a graph-based model to learn a
state representation of the multi-modal semantic features. To distinguish
between lane-following and lane-changing, we decompose an action of the
autonomous vehicle into a parameterized action structure that first decides
whether to change lanes and then computes an exact action to execute. A hybrid
reward function takes into account aspects of safety, traffic efficiency,
passenger comfort, and impact to guide the framework to generate optimal
actions. In addition, we design a regularization term and a multi-worker
paradigm to enhance the training. Extensive experiments offer evidence that
AUTO can advance state-of-the-art in terms of both macroscopic and microscopic
effectiveness.

自动驾驶是一项新兴技术，本论文提出了一种基于深度强化学习的参数化决策框架，AUTO，在感知多模态状态特征基础上设计了图形模型来学习多模态语义特征的状态表示，并通过混合奖励函数考虑安全、交通效率、乘客舒适度等因素，以生成最佳行动。通过广泛实验，证明了 AUTO 在宏观和微观效果上领先于现有技术。