YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
This addresses improved object detection for autonomous vehicles in low-light conditions, but it is incremental as it builds on existing sensor fusion techniques.
The paper tackles vehicle detection in night scenes by fusing camera and radar data with an uncertainty-aware method, showing significant performance gains over single-sensor baselines and competitive results with deep learning fusion approaches.
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we gather the outputs and corresponding meta information for both networks. For each predicted object, the gathered information is post-processed by a gradient boosting method to produce a joint prediction of both networks. In our experiments we combine the YOLOv3 object detection network with a customized $1D$ radar segmentation network and evaluate our method on the nuScenes dataset. In particular we focus on night scenes, where the capability of object detection networks based on camera data is potentially handicapped. Our experiments show, that this approach of uncertainty aware fusion, which is also of very modular nature, significantly gains performance compared to single sensor baselines and is in range of specifically tailored deep learning based fusion approaches.