EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception
This addresses energy efficiency for autonomous vehicles, but it is incremental as it builds on existing sensor fusion methods.
The paper tackles the problem of high energy consumption in autonomous vehicle perception by proposing EcoFusion, an energy-aware sensor fusion approach that adapts based on context, resulting in up to 9.5% better object detection with approximately 60% less energy and 58% lower latency on standard hardware.
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.