Training intelligent agents that can drive autonomously in various urban and highway scenarios has been a hot topic in the robotics society within the last decades. However, the diversity of driving environments in terms of road topology and positioning of the neighboring vehicles makes this problem very challenging. It goes without saying that although scenario-specific driving policies for autonomous driving are promising and can improve transportation safety and efficiency, they are clearly not a universal scalable solution. Instead, we seek decision-making schemes and driving policies that can generalize to novel and unseen environments. In this work, we capitalize on the key idea that human drivers learn abstract representations of their surroundings that are fairly similar among various driving scenarios and environments. Through these representations, human drivers are able to quickly adapt to novel environments and drive in unseen conditions. Formally, through imposing an information bottleneck, we extract a latent representation that minimizes the \textit{distance} -- a quantification that we introduce to gauge the similarity among different driving configurations -- between driving scenarios. This latent space is then employed as the input to a Q-learning module to learn generalizable driving policies. Our experiments revealed that, using this latent representation can reduce the number of crashes to about half.
翻译:在各种城市和高速公路情景中,可以自主驾驶的智能培训人员在过去几十年中一直是机器人社会的一个热门话题。然而,道路地形学和邻近车辆定位方面的驾驶环境的多样性使得这一问题非常具有挑战性。不用说,虽然自主驾驶的情景驱动政策很有希望,而且可以提高运输安全和效率,但显然不是普遍适用的可伸缩的解决办法。相反,我们寻求能够推广到新颖和看不见环境的决策计划和驾驶政策。在这项工作中,我们利用这样一种关键理念,即人类驾驶人员学习其周围环境的抽象描述,这些描述在各种驾驶情景和环境中相当相似。通过这些演示,人类驾驶人员能够快速适应新的环境,在看不见的条件下驾驶。正式地说,通过设置信息瓶颈,我们获得了一种潜在代表,可以最大限度地减少驱动情景之间的交通安全和效率。我们引入了一种量化方法,用以衡量不同驱动配置之间的相似性。然后,这种隐蔽空间被用作学习模块的输入材料,用于学习一般驾驶政策。我们的实验表明,使用这种潜在代表可以减少一半的碰撞次数。