## Method

Submitted on 7 Jan. 2019 03:50 by
Yucheng Wang (Shanghai University of Engineering Science, China)

 Running time: 0.1 s Environment: GPU @ 2.5 Ghz (Python + C/C++)

 Method Description: Input the image to get the result of the road detection. Parameters: learning_rate=0.0002, beta1=0.5 Latex Bibtex:

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 88.83 % 79.38 % 90.07 % 87.62 % 4.40 % 12.38 % UMM_ROAD 88.60 % 87.11 % 94.83 % 83.13 % 4.98 % 16.87 % UU_ROAD 88.46 % 75.82 % 87.21 % 89.74 % 4.29 % 10.26 % URBAN_ROAD 88.63 % 81.20 % 91.35 % 86.07 % 4.49 % 13.93 %
This table as LaTeX

## Behavior Evaluation

The following plots show precision/recall curves for the bird's eye view evaluation.

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## Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.

## Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.

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