This paper presents a latent space reinforcement learning method for interpretable decision-making of autonomous vehicles at highway on-ramps. This method is based on the latent model and the combination model of the hidden Markov model and Gaussian mixture regression (HMM-GMR). It is difficult for the traditional decision-making method to understand the environment because its input is high-dimensional and lacks an understanding of the task. By utilizing the HMM-GMR model, we can obtain the interpretable state providing semantic information and environment understanding. A framework is proposed to unify representation learning with the deep reinforcement learning (DRL) approach, in which the latent model is used to reduce the dimension of interpretable state by extracting underlying task-relevant information. Experimental results are presented and the results show the right balance between driving safety and efficiency in the challenging scenarios of highway on-ramps merging.

中文翻译：

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利用潜在空间强化学习为高速公路入口处的自动驾驶车辆做出可解释的决策


本文提出了一种潜在空间强化学习方法，用于高速公路入口匝道自动驾驶车辆的可解释决策。该方法基于潜在模型以及隐马尔可夫模型和高斯混合回归（HMM-GMR）的组合模型。传统的决策方法由于其输入是高维的并且缺乏对任务的理解，因此很难理解环境。通过利用HMM-GMR模型，我们可以获得提供语义信息和环境理解的可解释状态。提出了一个框架来统一表示学习和深度强化学习（DRL）方法，其中潜在模型用于通过提取底层任务相关信息来减少可解释状态的维度。给出了实验结果，结果表明在高速公路入口匝道合并的挑战性场景中，驾驶安全性和效率之间存在适当的平衡。