  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.
