Offloading Autonomous Driving Services via Edge Computing
Offloading Autonomous Driving Services via Edge Computing
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DOI：
10.1109/jiot.2020.3001218
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发表时间：
2020-06
影响因子：
10.6
通讯作者：
Mingyue Cui;Shipeng Zhong;Boyang Li;Xu Chen;Kai Huang
中科院分区：
文献类型：
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作者：
Mingyue Cui;Shipeng Zhong;Boyang Li;Xu Chen;Kai Huang
自动驾驶面临的一个关键挑战是处理大量传感器数据，并在真实的时间内做出安全可靠的决策。然而，自动驾驶车辆通常没有足够的车载资源来提供所需的计算能力。为了解决这个问题，本文提出了一种新的方法，将计算密集型的自动驾驶服务卸载到路边单元和云，以快速执行。我们的方法结合了整数线性规划（ILP）制定离线优化的调度策略和在线自适应的快速算法。我们验证了我们的技术与合成任务图和现实世界的部署。实验结果表明，该方法能有效地提高系统性能.
A key challenge for autonomous driving is to process a massive amount of sensor data and make safe and reliable decisions in real time. However, autonomous vehicles often have insufficient onboard resources to provide the required computation capacity. To address this problem, this article advocates a novel approach to offload computation-intensive autonomous driving services to roadside units and cloud for swift executions. Our approach combines an integer linear programming (ILP) formulation for offline optimization of the scheduling strategy and a fast heuristics algorithm for online adaptation. We verify our technique with both synthetic task graphs and real-world deployment. The experimental results show that our approach can improve system performance effectively.