## Method

Submitted on 14 Feb. 2019 17:03 by
Yecheng Lyu (Worcester Polytechnic Institute)

 Running time: 300 ms Environment: GPU @ GTX950M (Python +Tensorflow)

 Method Description: CNN+Distributed LSTM Parameters: Training Epoch=60, Optimizer=Adam, Learning Rate=1e-4 Latex Bibtex: @inproceedings{lyu2019road, title={Road Segmentation using CNN and Distributed LSTM}, author={Lyu, Yecheng and Bai, Lin and Huang, Xinming}, booktitle={2019 IEEE International Symposium on Circuits and Systems (ISCAS)}, pages={1--5}, year={2019}, organization={IEEE} }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 93.54 % 92.64 % 93.65 % 93.44 % 2.89 % 6.56 % UMM_ROAD 95.88 % 95.46 % 96.37 % 95.40 % 3.95 % 4.60 % UU_ROAD 92.95 % 91.93 % 93.32 % 92.58 % 2.16 % 7.42 % URBAN_ROAD 94.44 % 93.45 % 94.69 % 94.18 % 2.91 % 5.82 % UM_LANE 91.47 % 91.01 % 91.78 % 91.17 % 1.44 % 8.83 %
This table as LaTeX

## Behavior Evaluation

 Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40 UM_LANE 99.18 % 99.21 % 99.07 % 98.39 % 97.23 % 95.57 % 94.57 % 83.72 % 80.26 %
This table as LaTeX

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.

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## 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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