Segmentation

The MPD dataset mainly covers complex road scenes, such as road shadows, camera overexposure, rain, fog, lane contamination, etc. The robustness of the autonomous driving algorithm is precisely tested by these scenes. Algorithms that perform well on other related datasets may not perform well on MPD, whereas algorithms that perform well on MPD will be more likely to have strong adaptability in real scenes. Researchers in the field of autonomous driving are welcome to evaluate the performance of algorithms based on MPD!.

Metrics for segmentation

For segmenation, we will evaluate accuracy by [A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms].

图片3.png

For methods that output confidence maps, the classification threshold τ is chosen to maximize the F-measure, yielding Fmax:

图片4.png

The average precision(AP) will be defined by different recall values r:

图片5.png

Both AP and Fmax will be consider as the measurement for us to evaluate your algorithm. The full information for this content will be on our paper.

Road

  • Method

  • Code

  • Moderate

  • Runtime

  • Environment

  • Article

  • 1

  • PC-CNN-V2


  • 95.20%

  • 0.5s

  • GPU@2.5Ghz


  • 2

  • F-PointNet


  • 95.17%

  • 0.17s

  • GPU@3.0Ghz


Lane line

  • Method

  • Code

  • Moderate

  • Runtime

  • Environment

  • Article

  • 1

  • PC-CNN-V2


  • 95.20%

  • 0.5s

  • GPU@2.5Ghz


  • 2

  • F-PointNet


  • 95.17%

  • 0.17s

  • GPU@3.0Ghz


Tsinghua University

Haidian District, Beijing, 100084, P. R. China

Email:xyzhang@tsinghua.edu.cn

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