Code for our paper titled "HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception," accepted to be published in ICCPS 2022.
This repository contains the algorithmic implementation of our HydraFusion model. Our model is intended to be used with the RADIATE dataset available here: https://pro.hw.ac.uk/radiate/
hydranet.py -- contains the class HydraFusion, which defines our top-level model specification.
stem.py -- defines the stem modules in HydraFusion
branch.py -- defines the branches implemented in our model.
gate.py -- contains the gating module implementations.
fusion.py -- contains the definition of the fusion block along with the algorithms to fuse the bounding boxes output by each active branch.
The stems and branches are built using a split architecture implementation of Faster R-CNN with a ResNet-18 backbone. HydraFusion can be used with any image-based multi-modal dataset. In our evaluations we used two cameras, one radar sensor, and one lidar sensor as inputs to the model.
PyTorch 1.9