论文标题
高兴：扎根的分层自动驾驶进行复杂的服务任务
GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks
论文作者
论文摘要
鉴于自动驾驶汽车的当前点对点导航功能，研究人员正在研究需要车辆访问多个兴趣点的复杂服务请求。在本文中，我们为自动城市驾驶中的复杂服务请求开发了一个分层的计划框架，称为Glad。服务级别，行为级和运动级别的计划有三层。分层框架在其紧密的耦合中是独一无二的，其中不同的层传达了用户偏好，安全估计和系统优化的运动成本。从从驾驶行为收集的13.8k实例的数据集中学习，从视觉上讲，人们会以视觉上的依据。高兴使自动驾驶汽车能够有效，安全地满足复杂的服务请求。摘要和完整模拟的实验结果表明，我们的系统的表现优于文献中的一些竞争基线。
Given the current point-to-point navigation capabilities of autonomous vehicles, researchers are looking into complex service requests that require the vehicles to visit multiple points of interest. In this paper, we develop a layered planning framework, called GLAD, for complex service requests in autonomous urban driving. There are three layers for service-level, behavior-level, and motion-level planning. The layered framework is unique in its tight coupling, where the different layers communicate user preferences, safety estimates, and motion costs for system optimization. GLAD is visually grounded by perceptual learning from a dataset of 13.8k instances collected from driving behaviors. GLAD enables autonomous vehicles to efficiently and safely fulfill complex service requests. Experimental results from abstract and full simulation show that our system outperforms a few competitive baselines from the literature.
