In recent years, various state of the art autonomous vehicle systems and architectures have been introduced. These methods include planners that depend on high-definition (HD) maps and models that learn an autonomous agent's controls in an end-to-end fashion. While end-to-end models are geared towards solving the scalability constraints from HD maps, they do not generalize for different vehicles and sensor configurations. To address these shortcomings, we introduce an approach that leverages lightweight map representations, explicitly enforcing geometric constraints, and learns feasible trajectories using a conditional generative model. Additional contributions include a new dataset that is used to verify our proposed models quantitatively. The results indicate low relative errors that can potentially translate to traversable trajectories. The dataset created as part of this work has been made available online.

翻译：近年来,引进了各种先进的自主车辆系统和结构,这些方法包括依靠高清晰(HD)地图和模型,以端到端方式学习自主剂控制的模型和模型。虽然端到端模型旨在解决来自HD地图的可缩放性限制,但并不概括不同的机动车辆和感官配置。为克服这些缺陷,我们采用了一种方法,利用轻量的地图表示方式,明确执行几何限制,并使用一个有条件的基因模型学习可行的轨迹。其他贡献包括用于量化核实我们拟议模型的新数据集。结果显示,低相对错误有可能转化为可翻转的轨迹。作为这项工作一部分的数据集已在网上提供。