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Rural Intersection Decision Support (IDS) System: Status and Future Work
Alec GorjestaniArvind MenonPi-Ming ChengLee Alexander
Bryan NewstromCraig Shankwitz
University of MinnesotaITS Institute
Intelligent Vehicles Lab
Presentation Overview
Present status Validation/Characterization work Optimization work Data collection Data analysis
driver behavior 4 seasons of data, 24/7
Additional technical capabilities for CICAS Future Work Anecdotes
Present Status
All Systems working (showed yesterday) Open Architecture, we can integrate most any sensor,
communication system, processor, etc. Mostly off-the-shelf hardware Need to add
Wireless communication• Add hardware at radar station cabinets• Add hardware at main controller cabinet
Radar based vehicle classification• IV Lab has sensors• No J1708 message set for vehicle classification capability• Eaton-Vorad reorganizing, point of contact difficult to find
Present Status (cont’d)
Need to add Delphi Mainline Radar
• Purchase Order June 2004• 3 Sensors ordered for comparison to Vorad• Not yet arrived• Calls to Delphi not returned.
Would really like DSRC Test Kit (wink wink, nudge nudge)
Validation work: vehicle masking sensitivity
Radar sensitivity analysis “masking” of small radar Xsections by large ones Distance at which a motorcycle is masked by a
passenger car or truck
XUse DGPS equippedprobe vehicles to determineX at which motorcycle is masked
Validation work: radar detection/accuracy validation
Radar detection miss rate Use two reference sensors
as a measure against radar detection and range accuracy
Light beam known location Broken beam triggers
interrupt Compare radar data with
light beam (presence/location) and camera (presence/location)
Complements previous accuracy work
Camera
DGPS-Probe Vehicle
Radar
Vehicle/Gap Tracker
Tracker program Kalman filter-based state estimator
Noisy radar signals Internal sensor processing tends to “pull in”
vehicle position along sensor longitudinal axis If uncompensated, leads to lane assignment
errors (azimuth errors) States include vehicle location, vehicle
speed, vehicle heading, lane assignment Gap tracking = 1-vehicle tracking
Validation work: Vehicle/Gap Tracker Performance
All vehicles entering intersxn (both major and minor roads) assigned ID
All vehicles tracked within intersxn boundaries DGPS position compared to tracker position/ID for
lane changes, left turns, right turns, speed variations, etc.
ID 144 ID 225
DGPS-ProbeVehicle ID 169
Validation work: Vehicle/Gap Tracker sensitivity to loss of radar
Possibility radar may fail Tracker program designed to
Detect radar loss Compensate for radar sensor loss
Validate by disabling radar, running program, and comparing DGPS-based state estimate with tracker estimate
ID 144 ID 225
DGPS-ProbeVehicle
ID 169
Validation work: Vehicle Classification System performance
Compare radar & laser based system performance $1200 system vs. $13,000 system Determine performance envelope for Benefit:Cost analysis
Presence verified by light beam sensor Reference is visible light and IR Cameras aimed at minor
roads Image processing results compared to radar and Lidar
results. If three agree, performance is as expected. (Automation improves efficiency)
Discrepancies analyzed by human viewing captured images Identify problem areas Improve system capability
Vehicle Classification Validation Configuration
DGPS
HorizontalLaser
Vertical Laser
VehicleClassifying
RadarLaserPresenceDetector
Camera
Crossroads Trajectory Tracker Validation
Camera
DGPS-Probe Vehicle
Optimization work: Radar
Radar Sensor Spacing Intersection overbuilt Presently, 100% coverage Each sensor, 400’ range Tracker good enough for 500’ spacing? 600’
spacing?• Less spatial density => lower sensor cost
Less trenching Lower power Lower maintenance Lower cost
Optimization Work: Radar
Radar Sensors Considered Presently, Eaton Delphi Ordered
• Will be installed as soon as they arrive• Specifications close to Eaton Vorad• Considerably more expensive
Autosense? Decision based on VTTI’s results.• Autosense specs close to Eaton, but much longer range• Considerably more expensive than Eaton.• Geometric considerations – “seeing” over a hill
CA COTS Study Promising Technology
Optimization work: Communication
Wired vs. Wireless communication Original thought was to go wireless. However, given
effort to trench power, wired communication was an incremental cost.
Wireless• Pros:
Offers significant cost savings: i.e., no trenching• Cons:
Unknown reliability, sensitivity to local EMI conditions Sufficient bandwidth for present and future
applications?
Optimization work: Communication
Wired
Pros:
•Known bandwidth, known reliability, immunity from local EMI
Cons:
•Trenching costs, wire breakage, etc. (incremental cost not too great if power trenching done at same time).
Hardwired DSL to outside world for analysis, diagnostics, streaming video
Data Collection
Sensors Mainline Radar
• Location, speed, heading, lane Xroads: Camera, Laser, Radar
• Vehicle position, heading Minor road Laser, Radar
• Vehicle length, height profile Remote Weather Information System 0.9 miles North of Intersxn
Rates Most sensors at 10 Hz Laser at 30 Hz locally, processed data at 10 Hz Video at 30 Hz Weather at 15 minute intervals
Data Collection
Formats Engineering data stored as a database of “snapshots” of the
state of the intersection at 10 Hz Video data .mpg4 at 30 Hz. 5 Cameras 4 Gbyte data/day
Storage Local 80 Gbyte removable drive 2 Terabyte server at the U
Data Collection
Access DSL at the Intersection, monitor status remotely Mn/DOT truck station streaming video (maintenance, response)
Quality Assurance Data checks Periodic back-ups Self-diagnostics
Data Analysis
Understand Driver Behavior Statistics (Howard Preston’s work) showed that far-side (left
turn) crashes (70% in general, 80% at our intersection) far outweigh nearside crashes
WHY?• Right turns “easier?”
• Drivers take left in one motion rather than 2 (pause in median)? Distribution of gaps accepted by drivers: what gaps are being
taken?• for right turns
• for left turns
• for crossing intersection
• How Safe are these?
Data Analysis – cont’d
Correlate driver behavior with Vehicle type / size (vehicle classification) Driver age (macroscopic level, limited basis initially license
plate reader later) • Limited basis means grad student observer
Driver gender (limited basis initially, license plate reader later) Weather effects (R/WIS 0.9 Mile away), with in-road sensors
(collecting data already) Plan to collect data for 12 months, and analyze
incrementally Results directly applicable to Human Interface final
design/deployment and algorithm strategy Provides baseline measure for Field Operational Test
Future work
State Pooled Fund study underway 7 states included Goal is to instrument intersection in each state, determine regional
behavioral differences with drivers Portable Surveillance system
Sensors and comm. system built to analyze rural intersections (upcoming proposal to Minnesota Local Road Research Board)
Add microscopic driver data License plate reader can yield driver age/gender information
• Important to understand crash causality May eventually allow “tailoring” of warnings to specific driver Early analysis complete. Details to be worked out
• Data from DPS• Analysis
MN IDS Intersxn CICAS Capability - Communication
Wireless communication Presently
• 2.4 GHZ 802.11B Range about 1.6 Km
• 900 MHz RF Modem Range about 4-6 Km
Future• Mesh Networks• DSRC (5.9 GHz)• Emerging Technology
4 Foldable masts 4 transmit/receive sites Easy to change HW Not tied to a particular
architecture
MN IDS Intersxn CICAS Capability - Communication
Differential GPS corrections Intersection validation, mapping
Architecture Analysis Data broadcasts Client/Server Router/switch Bandwidth needs testing / analysis
Intersection state information comm. Collision avoidance Communication of data/in-vehicle warnings
MN IDS Intersxn CICAS Capability - Communication
Map Downloads Map detail (we have layers of
detail/info) Range – how much /fundamental
details needed for the intersection Timing (data well in advance of the
xroads) Handshaking/verification
• Validation that vehicles which need data have it
MN IDS Intersxn CICAS Capability - Sensors
Differential GPS corrections Methodology Correction source Validate accuracy requirements
Road Weather sensors Warnings / notifications to vehicles
Vehicles as sensors Road friction Position/speed/heading for collision avoidance
Other If it plugs in, we can use it.
Anecdotes
Local residents in favor of this technology “dangerous intersection!” “this will be great.” “will that slow traffic on 52?” “will that issue tickets?” “when are you going online?” “last winter, a LOT of cars went into the ditch…”
Two crashes have occurred since construction began in May Right angle crash resulted in injuries (stretcher and
ambulance)
This is a good summer job.