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Although autonomous vehicles (AVs) are expected to revolutionizetransportation, robust perception across a wide range of driving contextsremains a significant challenge. Techniques to fuse sensor data from camera,radar, and lidar sensors have been proposed to improve AV perception. However,existing methods are insufficiently robust in difficult driving contexts (e.g.,bad weather, low light, sensor obstruction) due to rigidity in their fusionimplementations. These methods fall into two broad categories: (i) earlyfusion, which fails when sensor data is noisy or obscured, and (ii) latefusion, which cannot leverage features from multiple sensors and thus producesworse estimates. To address these limitations, we propose HydraFusion: aselective sensor fusion framework that learns to identify the current drivingcontext and fuses the best combination of sensors to maximize robustnesswithout compromising efficiency. HydraFusion is the first approach to proposedynamically adjusting between early fusion, late fusion, and combinationsin-between, thus varying both how and when fusion is applied. We show that, onaverage, HydraFusion outperforms early and late fusion approaches by 13.66% and14.54%, respectively, without increasing computational complexity or energyconsumption on the industry-standard Nvidia Drive PX2 AV hardware platform. Wealso propose and evaluate both static and deep-learning-based contextidentification strategies. Our open-source code and model implementation areavailable at https://github.com/AICPS/hydrafusion.

HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception