## Method

[Anonymous Submission]

Submitted on 31 May. 2022 10:52 by
[Anonymous Submission]

 Running time: 7.1ms Environment: GPU @ 1.5 Ghz (Python)

 Method Description: The model separates the encoding process of road context, local details and road boundary information through a multi-branch structure shared by shallow features. And each branch is carefully designed with less computational overhead. Parameters: Epochs=100;Batch_size=16;Optimizer=Adam; Latex Bibtex:

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 94.92 % 89.56 % 95.38 % 94.47 % 2.08 % 5.53 % UMM_ROAD 96.88 % 92.93 % 96.98 % 96.78 % 3.31 % 3.22 % UU_ROAD 94.19 % 87.69 % 94.00 % 94.39 % 1.96 % 5.61 % URBAN_ROAD 95.64 % 90.30 % 95.78 % 95.51 % 2.32 % 4.49 %
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
This table as LaTeX

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

## 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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