Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.

翻译：尽管自主机器人前景广阔，但目前仍存在若干关键问题可能导致其性能与安全性受损。其中一项问题是延迟——我们发现即使是英伟达最新嵌入式平台也无法实时执行自动驾驶车辆的智能任务（如目标检测）。解决该问题的一个有前景的方案是边缘计算范式。通过与行业合作伙伴的协作，我们识别出现有边缘计算思维存在以下关键局限性：（1）服务器分布不够广泛，与车辆距离过远；（2）当前提出的边缘解决方案未能提供显著更优的性能及针对自动驾驶车辆的额外信息以证明其用户成本合理性；（3）现有最先进方案与自主系统中常用框架（特别是机器人操作系统ROS）不兼容。为解决这些问题，我们提出Genie技术——一种封装机制，可实现ROS中透明缓存（无需修改源代码）、分布式缓存构建（区别于传统集中式缓存方法），并构建联合三维物体地图，从而为特定区域内的所有车辆提供显著更低的延迟（即使在低功耗边缘服务器上）和更高质量的数据。我们在业界领先的嵌入式与边缘平台上完整实现该设计，采用主流自动驾驶软件Autoware，实验表明Genie可使Autoware视觉检测器平均延迟降低82%，平均实现31%的物体复用率（对传入请求最高可达67%），并随时间推移显著提升物体地图的置信度。