1. 2-2-1. Perception of Surroundings
This image is a concept image.
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2. 2-2-1-1. Vision System – Traffic Signal Perception
1) Traffic Light Perception
Multi images
Single image
Detection
Classification
Tracking & Decision
Left ?
Right ?
„Left‟
„Left‟, „Left‟, „Right‟, „Left‟…
“Left”
Color based detection
Learning based detection
Point tracking & Result voting
(RGB&HSV[1] threshold
→ Blob labeling)
(PCA[2] feature extraction
→ SVM[3] classifier)
(Deterministic tracking[4]
→ Result voting)
RGB
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HSV
PCA
SVM

3. 2-2-1-1. Vision System – Traffic Signal Perception
2) Traffic Sign Perception
Multi images
Single image
Detection
Classification
Tracking & Decision
…
Learning based detection
Learning based detection
Ring buffer & Result voting
(Haar-like feature extraction
→ Cascade classifier[6])
(PCA feature extraction
→ SVM classifier)
(Simple ring buffer
→ Result voting)
Haar-like
Cascade
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PCA
SVM

4. 2-2-1-1. Vision System – Traffic Signal Perception
3) Library for Developing Perception System
 We developed traffic signal perception system using
OpenCV.(http://opencv.org/) that serves qualified source code.
 OpenCV was helpful to us for following image process topics.
- Image Processing
- Machine Learning
- Object Detection
4) Camera for Developing Perception System
 Dragonfly2 is used as image sensor made by Point Grey.
(http://ww2.ptgrey.com/)
 SDK of Point Grey Camera supplies various functions for
developing system.
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5. 2-2-1-1. Vision System – Detection of Lane Markers
1) System Overview
selective Gaussian
spatial filters
Top view
thresholding
Hough
transform
RANSAC
line fitting
(1) Top View ↔ Perspective View

Remove perspective effects, using the inverse perspective mapping

Focus on only a subregion of the input image, which helps in reducing the run time

Reslult data can be transformed directly in real world coordinate
(2) selective Gaussian spatial filters ↔ Edge Detection

Simple and robust than the edge detection

Reduce computing time, using separable kernel

Optimized to detecting vertical, horizontal lines
Using separable kernel
Top View + Filtering
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Perspective View + Edge Detection
5

6. 2-2-1-1. Vision System – Detection of Lane Markers
2) Result
Lane, stop lane detection
Speed bump detection
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6

7. 2-2-1-2. Lidar System
A scan point clustering algorithm
Laser Data
Acquisition
Segmentation
&
Feature Extraction
Line-fitting & Coner fitting
Grid Map Generation
Object Queue
Mission Detection
Local Grid Map
Mission & Object
Information (Laser)
Decision System
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