.png){kind=icon}
返回
Computation and Sensor Offloading for Cloud-Based Infrastructure-Assisted Autonomous Vehicles
.png){kind=icon}
PRE
AI
DOI：10.1109/JSYST.2019.2959703.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing autonomous vehicles use a myriad of onboard sensors to detect objects and survey environment. Among these sensors, LiDAR is probably the most critical and a high-end LiDAR can cost several tens of thousands USD. High cost of sensors can be a formidable barrier to the proliferation of autonomous vehicles. This article proposes the idea of installing LiDAR sensors on roadside lamp posts for sharing among passing vehicles. This can avoid installing expensive sensors onboard each vehicle. Due to limited computation resources at lamp posts, the task of processing sensor data into environment maps for broadcast to vehicles are performed at edge-cloud servers. This article develops a scheme to identify which edge-cloud servers to host the virtual machines for which lamp posts, so that the total computation and communication cost can be minimized. The proposed scheme exploits reuse of existing data at servers for lower cost. The scheme is formulated as a mixed integer program, which is NP-hard. Therefore, we further propose an iterative algorithm with LP-relaxation to find good approximate solutions to the optimization. Extensive evaluation results confirm the effectiveness of the proposed algorithm. As an example, for a system with 50 lamp posts, the proposed algorithm can find solutions in less than 5 s while solving the original optimization using exhaustive search takes more than 24 h.
Keyword:
Servers
Autonomous vehicles
Laser radar
Cloud computing
Virtual machining
Real-time systems
Measurement by laser beam
Autonomous vehicle
cloud computing
edge-cloud server
optimization
virtual machine
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
