The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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1 Abstract-In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands.However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.

To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented LoPECS, a Low-Power Edge Computing System for real-time autonomous robots and vehicles services. The contributions of this paper are threefold: first, we developed a Heterogeneity-Aware Runtime Layer to fully utilize vehicle's heterogeneous computing resources to fulfill the real-time requirement of autonomous driving applications; second, we developed a vehicle-edge Coordinator to dynamically offload vehicle tasks to edge cloudlet to further optimize user experience in the way of prolonged battery life; third, we successfully integrated these components into LoPECS system and implemented it on Nvidia Jetson TX1. To the best of our knowledge, this is the first complete edge computing system in a production autonomous vehicle. Our implementation on Nvidia Jetson demonstrated that it could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proves the effectiveness of the proposed LoPECS system. INDEX TERMS Edge computing, QoE (quality of experience), low power, autonomous driving.

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