Rural Intersection Decision Support (IDS) System: Status and Future Work Alec Gorjestani Arvind Menon Pi-Ming Cheng Lee Alexander Bryan Newstrom Craig Shankwitz University of Minnesota ITS Institute Intelligent Vehicles Lab

Late Breaking Surveillance

Presentation Overview u Present status u Validation/Characterization work u Optimization work u Data collection u Data analysis v driver behavior v 4 seasons of data, 24/7 u Additional technical capabilities for CICAS u Future Work u Anecdotes

Present Status u All Systems working (showed yesterday) u Open Architecture, we can integrate most any sensor, communication system, processor, etc. u Mostly off-the-shelf hardware u Need to add v Wireless communication Add hardware at radar station cabinets Add hardware at main controller cabinet v 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) u Need to add v Delphi Mainline Radar Purchase Order June Sensors ordered for comparison to Vorad Not yet arrived Calls to Delphi not returned. u Would really like DSRC Test Kit (wink wink, nudge nudge)

Validation work: vehicle masking sensitivity u Radar sensitivity analysis v “masking” of small radar Xsections by large ones v Distance at which a motorcycle is masked by a passenger car or truck X Use DGPS equipped probe vehicles to determine X at which motorcycle is masked

Validation work: radar detection/accuracy validation u Radar detection miss rate v Use two reference sensors as a measure against radar detection and range accuracy v Light beam known location v Broken beam triggers interrupt v Compare radar data with light beam (presence/location) and camera (presence/location) v Complements previous accuracy work DGPS-Probe Vehicle Radar

Vehicle/Gap Tracker u Tracker program Kalman filter-based state estimator u Noisy radar signals v Internal sensor processing tends to “pull in” vehicle position along sensor longitudinal axis v If uncompensated, leads to lane assignment errors (azimuth errors) u States include vehicle location, vehicle speed, vehicle heading, lane assignment u Gap tracking = 1-vehicle tracking

Validation work: Vehicle/Gap Tracker Performance u All vehicles entering intersxn (both major and minor roads) assigned ID u All vehicles tracked within intersxn boundaries u DGPS position compared to tracker position/ID for lane changes, left turns, right turns, speed variations, etc.

Validation work: Vehicle/Gap Tracker sensitivity to loss of radar u Possibility radar may fail u Tracker program designed to v Detect radar loss v Compensate for radar sensor loss u Validate by disabling radar, running program, and comparing DGPS-based state estimate with tracker estimate

Validation work: Vehicle Classification System performance u Compare radar & laser based system performance v $1200 system vs. $13,000 system v Determine performance envelope for Benefit:Cost analysis u Presence verified by light beam sensor u Reference is visible light and IR Cameras aimed at minor roads u Image processing results compared to radar and Lidar results. If three agree, performance is as expected. (Automation improves efficiency) u Discrepancies analyzed by human viewing captured images v Identify problem areas v Improve system capability

Vehicle Classification Validation Configuration

Crossroads Trajectory Tracker Validation DGPS-Probe Vehicle

Optimization work: Radar u Radar Sensor Spacing v Intersection overbuilt v Presently, 100% coverage v Each sensor, 400’ range v 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 u Radar Sensors Considered v Presently, Eaton v Delphi Ordered Will be installed as soon as they arrive Specifications close to Eaton Vorad Considerably more expensive v 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 v CA COTS Study Promising Technology

Optimization work: Communication u Wired vs. Wireless communication v Original thought was to go wireless. However, given effort to trench power, wired communication was an incremental cost. v 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 u Wired v Pros: Known bandwidth, known reliability, immunity from local EMI v Cons: Trenching costs, wire breakage, etc. (incremental cost not too great if power trenching done at same time). u Hardwired DSL to outside world for analysis, diagnostics, streaming video

Data Collection u Sensors v Mainline Radar Location, speed, heading, lane v Xroads: Camera, Laser, Radar Vehicle position, heading v Minor road Laser, Radar Vehicle length, height profile v Remote Weather Information System 0.9 miles North of Intersxn u Rates v Most sensors at 10 Hz v Laser at 30 Hz locally, processed data at 10 Hz v Video at 30 Hz v Weather at 15 minute intervals

Data Collection u Formats v Engineering data stored as a database of “snapshots” of the state of the intersection at 10 Hz v Video data.mpg4 at 30 Hz. 5 Cameras v 4 Gbyte data/day u Storage v Local 80 Gbyte removable drive v 2 Terabyte server at the U

Data Collection u Access v DSL at the Intersection, monitor status remotely v Mn/DOT truck station streaming video (maintenance, response) u Quality Assurance v Data checks v Periodic back-ups v Self-diagnostics

Data Analysis u Understand Driver Behavior v Statistics (Howard Preston’s work) showed that far-side (left turn) crashes (70% in general, 80% at our intersection) far outweigh nearside crashes v WHY? Right turns “easier?” Drivers take left in one motion rather than 2 (pause in median)? v 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 u Correlate driver behavior with v Vehicle type / size (vehicle classification) v Driver age (macroscopic level, limited basis initially license plate reader later) Limited basis means grad student observer v Driver gender (limited basis initially, license plate reader later) v Weather effects (R/WIS 0.9 Mile away), with in-road sensors (collecting data already) u Plan to collect data for 12 months, and analyze incrementally u Results directly applicable to Human Interface final design/deployment and algorithm strategy u Provides baseline measure for Field Operational Test

Future work u State Pooled Fund study underway v 7 states included v Goal is to instrument intersection in each state, determine regional behavioral differences with drivers u Portable Surveillance system v Sensors and comm. system built to analyze rural intersections (upcoming proposal to Minnesota Local Road Research Board) u Add microscopic driver data v License plate reader can yield driver age/gender information Important to understand crash causality v May eventually allow “tailoring” of warnings to specific driver v Early analysis complete. Details to be worked out Data from DPS Analysis

MN IDS Intersxn CICAS Capability - Communication u Wireless communication v Presently 2.4 GHZ B  Range about 1.6 Km 900 MHz RF Modem  Range about 4-6 Km v 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 u Differential GPS corrections v Intersection validation, mapping u Architecture Analysis v Data broadcasts v Client/Server v Router/switch v Bandwidth needs testing / analysis u Intersection state information comm. v Collision avoidance v Communication of data/in-vehicle warnings

MN IDS Intersxn CICAS Capability - Communication u Map Downloads v Map detail (we have layers of detail/info) v Range – how much /fundamental details needed for the intersection v Timing (data well in advance of the xroads) v Handshaking/verification Validation that vehicles which need data have it

MN IDS Intersxn CICAS Capability - Sensors u Differential GPS corrections v Methodology v Correction source v Validate accuracy requirements u Road Weather sensors v Warnings / notifications to vehicles u Vehicles as sensors v Road friction v Position/speed/heading for collision avoidance u Other v If it plugs in, we can use it.

Anecdotes u Local residents in favor of this technology v “dangerous intersection!” v “this will be great.” v “will that slow traffic on 52?” v “will that issue tickets?” v “when are you going online?” v “last winter, a LOT of cars went into the ditch…” u Two crashes have occurred since construction began in May v Right angle crash resulted in injuries (stretcher and ambulance)

This is a good summer job.