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tfl_exploting_map_korea.md

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November 2020

tl;dr: Very thorough description of using HD map for TFL recognition.

Overall impression

Although the perception models the paper uses is quite outdated, it has a very clear discussion regarding how to use HD maps online.

Also refer to TFL map building with lidar for a similar discussion.

Key ideas

  • Prior maps (with lat/long/height of TFLs) improves accuracy of recognition and reduces algorithm complexity.
    • Task trigger: Recognition algorithms do not have to operate continuously as perception begins only when the distance tot he facing TLF is within a certain threshold
    • ROI extraction: this limits the search area in an image
    • Estimate the size of a TL
  • Procedure
    • RoI extraction with safety margin. Slanted slope compensation. Road pitch needs to be stored in the HD map as well.
    • Detection locates TFL in image
    • Classify state of TFL
    • Tracking estimate position of TFL. Threshold for association should adjust based on distance.

Technical details

  • The effect of pitch (on a bumpy road) is bigger for TFL at long distances. On average the pitch change could be up to +/- 2 deg.

Notes

  • Questions and notes on how to improve/revise the current work