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.