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Autonomous vehicles use multiple sensors, large deep-learning models, andpowerful hardware platforms to perceive the environment and navigate safely. Inmany contexts, some sensing modalities negatively impact perception whileincreasing energy consumption. We propose EcoFusion: an energy-aware sensorfusion approach that uses context to adapt the fusion method and reduce energyconsumption without affecting perception performance. EcoFusion performs up to9.5% better at object detection than existing fusion methods with approximately60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2hardware platform. We also propose several context-identification strategies,implement a joint optimization between energy and performance, and presentscenario-specific results.

EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception