Motion planning in urban environments: Part I
Motion planning in urban environments: Part I
复制标题
城市环境中的运动规划：第一部分
DOI：
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复制
发表时间：
2008
期刊：
影响因子：
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通讯作者：
Maxim Likhachev
中科院分区：
文献类型：
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作者：
D. Ferguson;T. Howard;Maxim Likhachev
我们提出了一个自动驾驶汽车导航通过城市环境的运动规划框架。这样的环境提出了许多运动规划的挑战，包括超可靠性，高速操作，复杂的车辆间的相互作用，停车在大型非结构化的地段，和受约束的机动。我们的方法结合了一个模型预测轨迹生成算法计算动态可行的行动与两个更高层次的规划生成长期计划在道路上和非结构化领域的环境。在这两部分的论文的第一部分，我们描述了基本的轨迹生成器和道路上的规划组成部分，这个系统。我们提供了ldquoBossrdquo的例子和结果，这是一款自动驾驶SUV，已经行驶了3000多公里，参加并赢得了城市挑战赛。
We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part I of a two-part paper, we describe the underlying trajectory generator and the on-road planning component of this system. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.