An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene. This requires jointly reasoning about the behavior of other actors while deciding its own actions as these two processes are inherently intertwined - a vehicle will yield to us if we decide to proceed first at the intersection but will proceed first if we decide to yield. However, this is not captured in most self-driving pipelines, where planning follows prediction. In this paper we propose a novel data-driven, reactive planning objective which allows a self-driving vehicle to jointly reason about its own plans as well as how other actors will react to them. We formulate the problem as an energy-based deep structured model that is learned from observational data and encodes both the planning and prediction problems. Through simulations based on both real-world driving and synthetically generated dense traffic, we demonstrate that our reactive model outperforms a non-reactive variant in successfully completing highly complex maneuvers (lane merges/turns in traffic) faster, without trading off collision rate.
翻译:在现实世界中运作的智能剂必须平衡地实现它的目标,不仅保持其自身的安全与舒适,而且保持周围场景的其他参与者。这要求共同推理其他行为者的行为,同时决定自己的行动,因为这两个过程有着内在的相互联系——如果我们决定首先在十字路口进行,一个飞行器就会屈服于我们,如果我们决定下降,它就会首先前进。然而,大多数自驾管道中并没有记录到这一点,规划也遵循预测。在本文件中,我们提出了一个新的数据驱动、反应性规划目标,使自驾驶车辆能够共同解释自己的计划以及其它行为者将如何应对它们。我们把这个问题设计成一种基于能源的深层结构模型,从观察数据中学习,并编码规划和预测问题。通过基于现实世界驱动和合成产生的密集交通的模拟,我们证明我们的反应模型在顺利完成高度复杂的动作(在交通中合并/在交通中产生)方面,不折不折不扣的变体,而无需交易碰撞率。