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

arXiv: https://arxiv.org/abs/2202.11330 · HTML: https://mlsanity.com/paper/2202.11330/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Selective Sensor Fusion for Neural Visual-Inertial Odometry](https://mlsanity.com/paper/1903.01534/) (2019)
- [Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off](https://mlsanity.com/paper/1607.05418/) (2016)
- [HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception](https://mlsanity.com/paper/2201.06644/) (2022)
- [SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control](https://mlsanity.com/paper/2107.10895/) (2021)
- [FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles](https://mlsanity.com/paper/2102.00702/) (2021)

## Built on by (most cited first, 13 of 13)

- [RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding](https://mlsanity.com/paper/2304.08600/) (2023)
- [Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion](https://mlsanity.com/paper/2410.12592/) (2024)
- [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://mlsanity.com/paper/2501.07451/) (2025)
- [Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems](https://mlsanity.com/paper/2207.08865/) (2022)
- [SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection](https://mlsanity.com/paper/2205.03974/) (2022)
- [Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks](https://mlsanity.com/paper/2410.16723/) (2024)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)
- [SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge](https://mlsanity.com/paper/2302.12493/) (2023)
- [CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion](https://mlsanity.com/paper/2306.15748/) (2023)
- [MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science](https://mlsanity.com/paper/2409.06558/) (2024)
- [Energy-Efficient Autonomous Driving with Adaptive Perception and Robust Decision](https://mlsanity.com/paper/2510.25205/) (2025)
- [CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving](https://mlsanity.com/paper/2608.09202/) (2026)
- [One Residual with Three Reuses: A Wristband Front End for Gesture Sensing](https://mlsanity.com/paper/2608.16542/) (2026)

## Abstract (arXiv)

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.