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动态轨迹生成和城市驾驶的条件生成模型
David Paz1, Hengyuan Zhang1, Hao Xiang1
1Autonomous Vehicle Laboratory, Contextual Robotics Institute, University of California San Diego, La Jolla, CA 92093, USA.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种使用OpenStreetMaps (OSM) 数据生成自动驾驶轨迹的新方法. 它可以在动态的城市环境中使用更少地图细节进行导航,提供实时性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 目前的自动驾驶系统通常依赖于高清地图 (HD).
- 动态的城市环境由于频繁的变化而带来挑战.
- 尽量减少对详细地图先验的依赖对于稳健的导航至关重要.
研究的目的:
- 为城市驾驶开发动态轨迹生成方法.
- 为了减少对自主导航的详细地图信息的依赖.
- 为城市驾驶行为创建条件生成模型.
主要方法:
- 使用粗的全球图表表征来生成轨迹.
- 对比了各种OpenStreetMaps (OSM) 数据表示形式.
- 制定了一个条件生成模型,以捕捉多式联运驾驶特征.
- 采用具有地面真相标签的全尺寸车辆收集数据.
主要成果:
- 证明了动态城市驾驶的潜在用例.
- 在复杂的场景中实现实时性能.
- 验证了拟议的生成模型战略的有效性.
结论:
- 拟议的方法为高清地图依赖系统提供了一个可行的替代方案.
- 该方法适用于动态城市环境中的实时自主导航.
- 发布的数据集和代码有助于进一步的研究和开发.
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