Extracting Urban Street Features Using Street Level LiDAR...

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EXTRACTING URBAN STREET FEATURES USING STREET LEVEL LIDAR DATA FOR CONNECTED VEHICLE APPLICATIONS Rakesh Nune Systems Engineer District Department of Transportation [email protected]

Transcript of Extracting Urban Street Features Using Street Level LiDAR...

Page 1: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

EXTRACTING URBAN STREET FEATURES USING STREET LEVEL LIDAR DATA FOR CONNECTED VEHICLE APPLICATIONS

Rakesh Nune Systems Engineer District Department of Transportation [email protected]

Page 2: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

Street Level LiDAR Data • DDOT started project in summer to extract

locations of pavements markings

• The intent of the project is to help in generation of MAP message

• The project was later expanded to identify traffic assets

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Street Level LiDAR Data

• DDOT Collected street level LiDAR data for over 300 miles.

• Street Level data provides rich 6 attribute data compared to traditional

• Apart from (x,y,z) data contains (R,G,B) values of each point

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Street Level LiDAR Data

Page 5: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

Street Level LiDAR Data • Extracting ground level images at each intersection

Page 6: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

Street Level LiDAR Data • LiDAR points plotted at a intersection

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Street Level LiDAR Data • Clustered LiDAR points at the intersection

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Image Recognition

Images generated from clustered points

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Image Recognition • Images are generated using above clustered points

• Neural network is trained to recognize which images are

pavement markings vs curb images

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Street Level LiDAR Data • The extracted data is used to train Neural network model

to classify pictures

• We had a success rate of 72%

• The results are plotted on the google maps

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Street Level LiDAR Data

Detected pavement marking on 16th St and Church St NW

Page 12: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

Street Level LiDAR Data

Detected pavement marking on 16th and Corcoran St NW

Page 13: Extracting Urban Street Features Using Street Level LiDAR ...itsmd.org/wp-content/uploads/2016-1B-Nune.pdf · DDOT Collected street level LiDAR data for over 300 miles. • Street

Street Level LiDAR Data

Detected pavement marking on 16th St and Church St NW

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Traffic Asset Recognition • The work is extended to recognize traffic assets

• Every object in LiDAR world is represented by a collection

of points

• Once pattern is extracted, it is feasible to extract the objects

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Traffic Asset Recognition • View of Lidar data at 16 St and Q St NW

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Traffic Asset Recognition Traffic and Light assets extracted from the data

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Future Work • The work will be continued to convert data in to MAP

message

• Asset recognition program will be enhanced to improve the accuracy and expanded on other corridors

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• Questions ?