EcoFusion：面向高效自动驾驶汽车感知的能量感知自适应传感器融合
EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception
AI总结：
本文提出EcoFusion能量感知自适应传感器融合方法，利用上下文动态调整融合策略，在Nvidia Drive PX2平台上以约60%更少能耗和58%更低延迟实现最高9.5%的目标检测性能提升。
摘要：
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并安全导航。在许多场景中，某些传感模态会对感知产生负面影响，同时增加能耗。我们提出EcoFusion：一种能量感知的传感器融合方法，利用上下文来调整融合方式，在不影响感知性能的情况下降低能耗。在行业标准的Nvidia Drive PX2硬件平台上，EcoFusion在目标检测方面比现有融合方法性能提升最高达9.5%，同时能耗降低约60%，延迟降低58%。我们还提出了几种上下文识别策略，实现了能量与性能之间的联合优化，并给出了针对特定场景的结果。
英文摘要：
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 propose EcoFusion: 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
