EcoFusion: energy-aware adaptive sensor fusion for efficient autonomous vehicle perception

EcoFusion: energy-aware adaptive sensor fusion for efficient autonomous vehicle perception
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EcoFusion:能量感知自适应传感器融合,实现高效的自动驾驶车辆感知

DOI:
10.1145/3489517.3530489
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发表时间:
2022
期刊:
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
影响因子:
--
通讯作者:
Faruque, Mohammad Abdullah
Faruque, Mohammad Abdullah
中科院分区:
--
文献类型:
--
作者:
Malawade, Arnav Vaibhav;Mortlock, Trier;Faruque, Mohammad Abdullah

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自动驾驶汽车使用多个传感器、大型深度学习模型和强大的硬件平台来感知环境和安全导航。在许多情况下,一些感知模式在增加能源消耗的同时对感知产生负面影响。我们提出了EcoFusion:一种能量感知的传感器融合方法,该方法利用上下文来适应融合方法,在不影响感知性能的情况下降低能量消耗。在行业标准的NVIDIA Drive PX2硬件平台上,EcoFusion在目标检测方面的性能比现有的融合方法高出9.5%,能耗降低约60%,延迟降低58%。我们还提出了几种上下文识别策略,实现了能量和性能的联合优化,并给出了特定场景的结果。
Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely. In many contexts, some sensing modalities negatively impact perception while increasing energy consumption. We proposeEcoFusion: an energy-aware sensor fusion approach that uses context to adapt the fusion method and reduce energy consumption without affecting perception performance. EcoFusion performs up to 9.5% better at object detection than existing fusion methods with approximately 60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2 hardware platform. We also propose several context-identification strategies, implement a joint optimization between energy and performance, and present scenario-specific results.
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发表时间: 2012
期刊: DAC Design Automation Conference 2012
影响因子: --
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