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Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems

December 2019

tl;dr: Early fusion of radar and camera via range-azimuth map + IPM feature concatenation.

Overall impression

This is follow up work on Qualcomm's ICCV 2019 paper on radar object detection. The dataset still only contains California highway driving.

Radar is acquired under the same technical spec as in radar object detection. The addition of camera info does not boost the performance of radar a lot (only about 0.05%), and it suffers less if the camera input is set to 0. The camera info did help reducing the lateral error.

Training the camera branch in advance (similar to BEV-IPM) and freeze it during joint training yielded the best results.

Key ideas

  • Two parallel branches, then concatenating final features from both backbones for detection head.
    • Radar: range azimuth map
    • Camera: IPM.
    • Range: 46.8 x 46.8 m
    • The resolution of the feature maps match so they can be concat or added together.
  • Empirically, placing polar to cartesian early in the intermediate feature layers in the radar branch gave the best performance. --> this is different from the method in deep radar detection.
  • Placing the IPM transformation directly to the camera image works best.
  • Camera branch is harder to train than radar branch. The jointly trained network is more robust toward failure of camera branch.
    • visually occluded objects may still return radar signal
    • highway scene is easier for radar as there is limited clutter.
    • Distant objects mostly depend on radar.

Technical details

  • Traditional radar literature refers to detection as the task of returning a spatial point, in contrast to the computer vision community where detection usually returns a bbox.
  • Synchronization: extract one radar frame per 100 ms, get nearest neighbor camera frame, and use interpolated lidar annotation.
  • In lidar, points from objects at the boundary of a frame might have moved significantly. This discontinuityis take care of in lidar processing pipeline.
  • Lidar GT is human corrected, non-causal processing and temporal tracking (both forward and backward?) is used.

Notes

  • Background
    • traditionally, early fusion allows low-level fusion of the features resulting in better detection accuracy.
    • radar data is typically processed using a CFAR algorithm to convert the raw data into a point cloud which separates the targets of interest from the surrounding clutter.
  • Synchronization: why not use lidar as base line? --> Maybe still inherent misalignment between radar and lidar annotation.