EcoFusion: エネルギー効率を考慮した適応型センサフュージョンによる自動運転知覚
EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception
状況に応じてセンサ融合の手法を切り替え、知覚性能を保ちながら消費エネルギーを削減するEcoFusionを提案。Nvidia Drive PX2上で物体検出精度が最大9.5%向上し、エネルギー約60%減・遅延58%減を実現した。
著者: Arnav Vaibhav Malawade, Trier Mortlock, Mohammad Abdullah Al Faruque
分類: cs.CV, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
