sensor technology at the smart public intersection ...€¦ · 18.09.2013 sensor technology at the...

Sensor Technology at the Smart

Public Intersection Aschaffenburg

Michael Goldhammer, Ulrich Brunsmann, Konrad Doll

Hochschule Aschaffenburg

Sensorik und Intelligenz der

öffentlichen Testkreuzung Aschaffenburg

Overview

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• Cooperative perception - Concept

• Test setup at public intersection

• Sensors

• Experimental results

Cooperative perception -

Concept

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MMI

Driver Intention

Risk Analysis

Scene Interpretation

Inter-Vehicle (Cooperative) Perception

I2V- and V2V-Communication

Use Cases Safety Functions

Intersection-based

Local Perception

Vehicle-based

Local Perception,

Self-Localization

Intersection based perception

• Test site

• Hardware architecture

• Laserscanners

• Cameras

• Calibration

• Experiments and results

Test setup at public intersection

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Intersection in an

urban area

• 5 and 3 lanes

• 3 crosswalks

• Bicycle lane

• Approx. 23,000

vehicles per day

UAB

Architecture of perception system

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Traffic

Lights

co

mp

lem

en

tary

Laserscanner subsystem

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14 SICK LD-MRS research multilayer laserscanners:

• 4 horizontally aligned for central intersection area and crosswalks

• 8 vertically aligned for incoming lanes, range up to 100m

• 2 for sidewalks, crosswalks and bicycle lane

Simulated fields of view Measured Lidar reflections

Camera subsystem

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10 monochrome cameras:

• 4 wide angle cameras for central intersection area

• 3 cameras for intersection approaches

• 1 top view camera for critical area

• 2 high definition cameras for crosswalks and critical area,

representing a wide angle stereo system

Simulated fields of view

Sensor mounting positions

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Experimental results:

Evaluation of cross calibration

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Spatial alignment

Target position by standard camera

system (green) and person‘s position by

laserscanner system (blue) (extract)

x-Coordinate (m)

y-C

oo

rdin

ate

(m

)

Tracking of camera reference target

Experimental results:

Evaluation of cross calibration

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Temporal alignment

Results

Standard deviation in position

∆𝑥2 + ∆𝑦2 < 17cm

Temporal alignment < 80 ms

No

rm. C

ross-C

ova

ria

nce

of LS

vs. S

D c

am

era

sam

ple

s

Time lag of position on y-axis

In multiples of LS sample time (80ms)

Experimental results:

High definition stereo system

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Target device placed at 10 globally measured (DGPS)

positions in the fields of view of both cameras

Results

Absolute position error of the

HD camera stereo subsystem

∆𝑥2 + ∆𝑦2 + ∆𝑧2

< 20mm (on ground plane)

< 30mm (2 m above ground)

Application:

Pedestrian intention detection

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Visualization of estimated trajectory (sigmoid model)

(Slow motion 1/10, 0.2 s/div)

Application:

Pedestrian intention detection

2.4 2.0 1.6 1.2 0.8 0.4 s

Comparison: Estimation and reference measurement

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[1] S. Köhler, M. Goldhammer, S. Bauer, S. Zecha, K. Doll, U. Brunsmann, K. Dietmayer:

“Stationary Detection of the Pedestrian’s Intention at Intersections,” accepted for publication in:

IEEE Intelligent Transportation Systems Magazine ITSC 2012 Special Issue, invited Paper.

Conclusion

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• Comprehensive sensor network at public urban intersection

• Adaption to local surroundings by 3D software simulation

• Occlusion resistant vision range

• Calibrated system provides positions in world coordinates

Thank you!

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