Road Traffic Scanner (TScan) for
Collecting High-Resolution Data for
Traffic and Safety Analysis
Presented by:
Mario A. Romero
Other team members:
PI: Andrew P. Tarko
Kartik B. Ariyur
Vamsi Krishna Bandaru
Cheng Liu
Purdue University
Agenda
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•
•
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What is TScan?
Objectives
Prototype
Application
What is TScan?
• LiDAR-based low-cost traffic
scanner
• Collects microscopic traffic
data from a road intersection
area
Objectives
• Develop object identification and tracking
algorithm
• SSAM Compatible
• Interface between the data processing module and the
existing Surrogate Safety Assessment Model (SSAM)
– Freely available public domain software developed by Siemens
ITS with FHWA funding
– SSAM is capable of converting the microscopic traffic information
produced with TScan into meaningful safety-related information
such as traffic conflicts and other risky interactions
• Demonstrate two applications
– Counting turning vehicles at intersections
– Counting traffic conflicts at intersections
Prototype
• Traffic Mobile Laboratory,
Center for Road Safety,
Purdue University
– LiDAR
– 2 HD cameras
– Data storage units
– Computer
– UPS
– 42 foot mast
Process
• Data acquisition and reduction
• Data Processing
– Background (Spherical vs Cartesian)
– Coordinates transformation
– LiDAR self calibration process
– Intersection representation
– Objects detection
– Tracking objects for SAMM
Data Acquisition
• LiDAR
~ 11.2 Gb/Hour
– 64 laser sensors
• 360 degrees field of view (azimuth)
• 0.09 degrees angular resolution (azimuth)
• 26.8 degrees vertical field of view
– 300-900 RPM = 5-10 Hz field of
view update
– 1.33 million points per second
• 2 HD out door cameras
– PTZ control
– Day/night function
– Zoom 18x
• Data reduction is required
+
~ 0.6 Gb/Hour * 2 cameras
͌
12.4 Gb/Hour
~300 Gb/day
~2.5 Tb/week
Data Acquisition
Video
LiDAR
Coordinates
2D pixels
3D - Spherical (r, ϕ, θ)
millimeters & degrees
Frame rate
30 fps (10-60)
10 fps (5-15)
Measures
Color
Geometry
Environmental
Affected by light, fog
Not affected by light or fog
Processing
Image processing,
artificial vision
Numeric
Data Reduction
• Basic data reduction
– Angular data reduction
(azimuth)
• Eliminate not required
data (2/3)
• User defined
• Real time
• Other options
– Compress
Background
• Statistical analysis of each beam over time
allows us to remove moving objects
Coordinates Transformation
• Basic Spherical to Cartesian
𝑥 = 𝑟 ∙ cos 𝜑 ∙ cos 𝜃
𝑦 = 𝑟 ∙ cos 𝜑 ∙ s𝑖𝑛 𝜃
𝑧 = 𝑟 ∙ sin 𝜑
• Additional corrections
– Laser offset
– Laser rotational
correction
Self-calibration
• The LiDAR gives 3-D point clouds and 1-1 correspondence
with physical world, but posed several problems:
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
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LiDAR motion affects measurements significantly
The same points are not scanned by the LiDAR each time
Reflections are diffuse and do not all return to the LiDAR
• Solution:


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Calibrate out LiDAR motions by detecting and tracking invariant planes
Perform motion estimation through detecting and tracking moving
planes
All calculations are based on least squares algorithms run in real-time
All results come with clear mathematical properties
Intersection representation
• User defined borders
• Best fitting planes
– Eliminating suspended objects
– Takes the results of the self-calibration process
Object detection
• Objects = Frame – Background
vs.
Detection of Moving planes
– Noise
• Frames &
• Background
• Project objects to
intersection plane
?
=
Tracking Objects for SSAM
• Identify objects
Ti
vs.
• Track over time
– Predict new position
– Adjust position
• The lower interval the
better
– Backtracking
• Create a trajectory file
to interact with SSAM
Ti + Δt
Conclusions
• The credibility and accuracy of simulations or Traffic
Conflict Techniques observations may be not adequate
for trustworthy modeling and evaluation
– TScan addresses the above problems
• Post-processing of the TScan-collected data produces
accurate vehicle dimensions and trajectories that can
be used in standard evaluation tools such as SSAM
• Other potential applications
– Pedestrian-vehicle interaction studies
– Traffic signals studies
– Intersection performance evaluation
T-Scan
Road Traffic Scanner (TScan) for Collecting HighResolution Data for Traffic and Safety Analysis
Questions