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

TL;DR

EcoFusion is an adaptive sensor fusion method for autonomous vehicles that reduces energy consumption by up to 60% while maintaining or improving perception accuracy through context-aware adjustments.

Contribution

It introduces EcoFusion, a novel energy-aware sensor fusion approach that dynamically adapts based on context to optimize energy use without sacrificing perception quality.

Findings

EcoFusion outperforms existing methods by 9.5% in object detection accuracy.

It reduces energy consumption by approximately 60%.

It achieves 58% lower latency on Nvidia Drive PX2 hardware.

Abstract

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.