We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen's ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.
翻译:我们考虑的是自动生成现实交通场景的问题。 现有方法通常根据一套手工制作的杂技,将行为方插入现场,它们模拟真实交通场景的真正复杂性和多样性的能力有限,从而导致合成交通场景与真实交通场景之间的内容差距。 因此,现有的模拟器缺乏培训和测试自驾驶车辆所必需的真实性。 为了应对这一限制,我们展示了SceneGen, 这是一种神经自动反射式的交通场景模型,避免了规则和超常性的必要性。 特别是,鉴于自我车辆状态和周围高定义地图,SceenGen将不同班级的行为者插入现场并合成其大小、方向和速度。 我们展示了两个大型数据集SceneGen的忠实模拟真实交通场景分布的能力。 此外,我们展示了SceenGen与感官模拟可以用来培养对真实世界具有普遍性的感知觉模型。