The KITTI Vision Benchmark Suite

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

Benchmark	PRE-20	F1-20	HR-20	PRE-30	F1-30	HR-30	PRE-40	F1-40	HR-40

This table as LaTeX

Road/Lane Detection

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

This figure as: png eps pdf

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.

This figure as: png