Method

LidarHistogram[la] [LidarHisto]
http://www.chenliang.me/blog/wp-content/uploads/2017/07/lidarhistogram.pdf

Submitted on 22 Mar. 2016 12:27 by
Liang Chen (Nanjing University of Science and Technology)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Lidar guided environment perception framework for
automomous driving
Parameters:
none
Latex Bibtex:
@INPROCEEDINGS{7989159,
author={L. Chen and J. Yang and H. Kong},
booktitle={2017 IEEE International Conference on
Robotics and Automation (ICRA)},
title={Lidar-histogram for fast road and obstacle
detection},
year={2017},
pages={1343-1348},
keywords={Hazards;Histograms;Laser
radar;Lasers;Roads;Three-dimensional displays;Two
dimensional displays},
doi={10.1109/ICRA.2017.7989159},
month={May},}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 89.87 % 83.03 % 91.28 % 88.49 % 3.85 % 11.51 %
UMM_ROAD 93.32 % 93.19 % 95.39 % 91.34 % 4.85 % 8.66 %
UU_ROAD 86.55 % 81.13 % 90.71 % 82.75 % 2.76 % 17.25 %
URBAN_ROAD 90.67 % 84.79 % 93.06 % 88.41 % 3.63 % 11.59 %
This table as LaTeX

Behavior Evaluation


This table as LaTeX

Road/Lane Detection

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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