j.r. richardson principal systems engineer raytheon htms, fullerton, ca (714) 446-4360...
TRANSCRIPT
J.R. RichardsonPrincipal Systems Engineer
Raytheon HTMS, Fullerton, CA
(714) 446-4360
Copyright © 2010 Raytheon Company. All rights reserved.
Intersection Safety –Ideas, Innovation, Technology
3 May 2010
SPEAKER’S ROLE : Technical/Scientific Sessions are paper sessions targeted at the ITS professionalsand scholars to learn more from the authors about the technical details of the latest transportation technologies,as well as progress and results of various research programs, field tests, and other initiatives nationwide.
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Current State Assessment
– Traffic fatalities are generally categorized as resulting from: Vehicle collisions (vs. another vehicle) Roadway departures Pedestrian collisions
Vehicular Safety Applications
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Problem Domain Description
% Fatalities that include Pedestrians by State
Pedestrians account for a significant percentage of urban traffic fatalities
Reducing traffic related fatalities is the top priority of the U.S. DOT
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Challenges of Modeling “Systems”
Engineers want to model kinematics:
xt = a0 t2 + v0 t + x0
– Objects with inertia conform Cars
– Objects with “Free Will” do not Humans
One of the reasons Ped Safety apps have not advanced like V2V & CICAS…
??
t0 t0+2 t0+8t0+6t0+4
V = 50 mphW = 2,000 lbs.
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Physics 101 – Continuous Coordinate Systems
y
X
Z
X/Y/Z, North/East/Up, Lat/Lon/Alt, Range/Azimuth/Elevation Continuous coordinates means 3 “Floating Point” values x 3 for position, speed, acceleration = 9 values… x 8 byte “doubles” for my software app = 72 bytes/object x How many objects am I tracking and predicting for? Is there an alternate approach to streamline interfaces?
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The Local Area Abstraction (LAA)
CPWS uses the Intersection Occupancy Map (IOM) LAA
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Physics 101 – Kinematic Extrapolation
Engineers want to extrapolate
xt = a0 t2 + v0 t + x0
… or at least want continuous, bounded solution sets… How can we possibly model non-kinematic, non-continuous
behavior of objects in a way that is meaningful?
“Merge Plot”anticipatedat time t = n
And what aboutexternal influences?
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Non-Kinematic & Semi-Kinematic Behaviors
Vehicles driven by humans are “semi-kinematic” at best “Free Will” pedestrians may also be externally influenced
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Non-Kinematic & Semi-Kinematic Abstraction
The Pedestrian Occupancy Map (POM) approach
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2a – Envision a pedestrian-centric mini-LAA
2b – At time t = now, probabilities are 1 or 0“He’s gotta be somewhere!”
2c – Probabilities at time t = future…
POM may depend on External Influences!
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Non-Kinematic & Semi-Kinematic Abstraction (2)
The POM is then used to populate the future-state IOM LAA
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Non-Kinematic Collision Determination
The future-state LAA is checked vs. projected vehicle state
some futuretime IOM…
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UPWS’ Computation-Less Collision Detection
Introduce the Collision Determination Pattern Vector (CDPV) For each >0 probability in IOM cell “i”, CDPV(i*) = 1; else 0 Search or “hash” CDPV for collisions of interest… “0x05” is a Pedestrian vs. Vehicle collision to be checked
Can easily configure to detect other collisions of interest “0x08” bit set is a Vehicle vs. Vehicle collision to be checked Flexible, configurable architecture supports “CICAS” too!
… 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0…
… 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0…
single pedestriansingle vehicle
single vehiclemultiple vehicle
OC group cell “i” bits
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Rule-Based Configuration vs. Hard Requirements
Too tall tobe human
Too short tobe human
Max
Min
Open Architecture uses configurable Object Classification (OC) Rule files – multiple attributes, results w/ “confidence”
As coded requirements, adding any “new humans” means new software
Rule-Based configuration allows quick updates with no new software
Result confidence (e.g. 90%) allows multiple rules / composite rules to drive final OC calculation “winner”
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UPWS’ Teachable Object Classifier Visualizer Tool shows color-coded OC results to human teacher Click on incorrect results and select correct result in drop-down Confidences of satisfied rules are “tweaked” up/down for improvement
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UPWS’ Adaptive Universal Prediction Logic (UPL)
Compares predicted results to actual and auto-corrects!
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2a – POM prediction for t0+ 4 seconds
2b – 4 sec later, “now” probabilities are 0 & 1“He’s gotta be somewhere!”
2c – Original POM probabilities are adjusted- up slightly for the observed result- down slightly for all other results
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Conclusions & Possibilities… Information & data architectures are critical to complex systems Advanced, “outside-the-box” methods such as LAA, POM and Rule-Based open system architectures enhance
flexibility and configurability, and promote technical interoperability with other advanced systems.
+ +
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J.R. Richardson
Raytheon, Fullerton, CA• Principal Systems Engineer, Highway Transportation Management Systems (HTMS), 2006-2010
• Strategic Technology & Business Development Team Cooperative Pedestrian Warning System (CPWS/UPWS) Lead Engineer• OmniAir DSCR Certification Committee – System Safety Certification SME• Vehicle Infrastructure Integration (VII) Proof of Concept (POC)
• Principal Software Engineer, Navigation & Landing Systems (NLS), 1996-2006• Wide Area Augmentation System (WAAS) & related Space-Based Augmentation Systems (SBAS)
– System Safety Certification Team Lead, 2003-2006 – Correction & Verification (C&V) CSCI, Safety Processor Software Team Lead (1996-2005)
In the prior Millennium…• Extensive experience in flight simulation (Northrop and Hughes Training)• Aircraft avionics design and implementation (Northrop and Lockheed Martin)• Air traffic management systems development (Hughes/Raytheon)
California State Polytechnic University, Pomona• B.S. (1984) Aerospace Engineering
• Minor in Computer Science• M.S. (1992) Aerospace Engineering
• Emphasis in Control Systems Design
Who is this guy?
J.R. RichardsonPrincipal Systems EngineerHighway Transportation Management SystemsOffice: (714) 446-4360Cell: (951) [email protected]
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Questions & Answers
There must be SOMETHING you want to ask!