1. Human Factors Progress IDS Project Nicholas Ward Jason Laberge Mick Rakauskas HumanFIRST Program

2. Unsignalized Intersections: Previous work on DII’s Collision Countermeasure System Prince William Co., Virginia Intersection Collision Avoidance Warning System Norridgewock, Maine Limited Sight Distance Warning Signs Gwinnett County, Georgia

3. Collision Countermeasure System Prince William Co., Virginia Thru-STOP at two 2-lane roads Focus on warning major approach Data Collected: Speed (intersection arrival, reduction) Projected time to collision (PTC)

4. Human machine interface evaluated for Collision Countermeasure System (CCS) Prince William County, Virginia  Aden road (major) & Fleetwood Drive (minor) intersection located on plateau with restricted sight distances.  Drivers on minor leg often had difficulty sensing safe gap On minor legOn major leg

5. Collision Countermeasure System (minor approach) (major approach)

6. Methods & Findings Collected data before, acclimation, 4-mo., & 1-yr. after installation Results: Sizeable novelty effect Smaller number of high-speed vehicles Encouraged safer driving in presence of minor road traffic One crash during inoperative period

7. Intersection Collision Avoidance Warning System Norridgewock, Maine Thru-STOP at two 2-lane roads Focus on warning minor approach Data: Observational techniques Surveys

9. Methods & Findings Observational: Traffic Conflict Technique (TCT) Swedish Technique (adds Time to Collision) Results: Reduction in conflict potential Half of drivers waited for sign Half proceeded after vehicle passed through intersection No reduction main road speeds More queued traffic on minor approach

10. Limited Sight Distance Warning Signs Gwinnett County, Georgia 18 Thru-STOPs at two 2-lane roads Chosen based on minimum sight distance guidelines & reported problems Warnings for major &/or minor approaches Signs considered interim solution

11. STOP

12. Methods & Findings Comparison of crash records 3-yrs. before to 3-yrs. after installation Crashes reduced from 7 to 1 At one Intersection Most others reported 0 crashes Overall inconclusive findings Many variations of system Low crash rates

13. Human Factors Tasks Analyze problem Task analysis “What are drivers doing wrong?” “Who is at most risk?” Driver model (Information Process) “Why are they doing it wrong?” “What information could support correct behavior?” Previous solutions “What has not worked before?” Simulate case site Propose interfaces and simulate candidate Evaluate candidate interface

14. Task Analysis Detect intersection Decelerate and enter correct lane Signal if intending to turn Detect and interpret traffic control device Detect traffic and pedestrians Detect, perceive, and monitor gaps Accept gap and complete maneuver Continue to monitor intersection

15. Human factors issues In Minnesota, most drivers stop before proceeding (Preston & Storm, 2003) 57% stopped in 2296 rural thru-STOP accidents 87% of right angle crashes at US 52 and CSAH 9 occurred after the driver stopped NOT a violation problem Instead, a gap acceptance problem Detecting vehicles and presence of gaps in traffic Perceiving gap size Judging safe gaps

16. Information Needs A. Vehicle Detection B. Convey speed/distance/arrival time of lead vehicle C. Convey lead gap size D. Judge “safe gap” (and display location in traffic)

17. Information Needs Most prior systems limited to emphasizing: 1. Presence of intersection and traffic control device. 2. Presence of approaching cars. 3. Approach speed of cars. Given that awareness of intersection and compliance with TCD’s is not the problem in our case, method 1 above will not benefit safety. To the extent that drivers are at risk because of problems with more complex information needs (C and D), simply presenting information about vehicle detection will not benefit safety.

18. Information Needs A. Vehicle Detection B. Convey speed/distance/arrival time of lead vehicle C. Convey lead gap size D. Judge “safe gap” (and display location in traffic) Since the research does not give evidence of the relative importance of these factors toward crash risk, it is necessary to design options for ALL of the above. Note also, that the highest level (D) also satisfies the lowest level (A), but NOT conversely.

19. Target Population Older drivers (> 65 years) have a high crash risk at intersections Drivers > 75 years had greatest accident involvement ratio (Stamatiadis et al., 1991) Drivers > 65 years - 3 to 7 times more likely to be in a fatal intersection crash (Preusser et al., 1998) Drivers > 65 years - over-represented in crashes at many rural intersections in Minnesota (Preston et al., 2003)

20. Intersection Selection: Based on State-wide Crash Analysis Analysis of present conditions and intersections …. Howard Preston, lead Identification of Experimental Site: Minnesota Crash Data Analysis 3,784 Thru-STOP Isxns in MN Hwy System were evaluated Total > CR (% of total) 2-Lane - 3,388 | 104 (~ 3%) Expressway - 396 | 23 (~ 6%)

21. Candidate Intersections: At-Fault Driver Age Source: Mn/DOT 2000 – 2002 Crash Data

22. Candidate Intersections: Crash Type Distribution Source: Mn/DOT 2000 – 2002 Crash Data

23. Selected Intersection

24. Sight distance restricted on the W approach at CSAH 9 Note differences in N and S vertical alignments Elevation

25. Intersection Simulation Task

27. Interface Task Human factors analysis of crash problem Task Analysis Driver Model Abstraction Hierarchy Expert panel review of concepts Everyone had own perspective No consensus Candidate set proposed based on information needs: Detect vehicle Present speed and time Present gap size Specify safe gap Sign formats consistent with MUTCD (shape, color)

28. Four Prototypes Static Warning New warning sign Sign conforms to human factors criteria for warning labels Low cost solution (baseline) Split-Hybrid Arrival time countdown for lead vehicle Prohibitive symbol relative to maneuvers based on near and far-side traffic conditions. Hazard Beacon Flashing red beacon activates when intersection is unsafe System tracks speeding or arrival time of lead vehicle Speedometer Speed monitor for lead vehicle Flashes red when near or far-side vehicle is speeding

29. Expert Review 19 evaluations sent out (37 % response rate) 2 Minnesota IDS team 5 Expert panel No consensus

30. Static Warning Sign STOP CAU FAS <---- ----> DIVIDED HIGHWAY CAUTION FAST CROSSING TRAFFIC BE CAREFUL STOP

31. Hazard Beacon STOP The light above the sign is solid white at all other times to indicate the system is functional A light above the STOP sign flashes red if any “lead” vehicle is speeding and/or if an unsafe gap is detected in either direction STOP Dangerous Crossing Flashing Red CAU FAS <---- ----> STOP DIVIDED HIGHWAY DANGEROUS CROSSING WHEN FLASHING RED

32. Split-Hybrid STOP VEHICLE WILL ARRIVE FROM THE RIGHT IN SECONDS VEHIC WILL ARRI FROM LEFT IN SECONDS VEHIC WILL ARR FROM LEFT IN SECONDS VEHICLE WILL ARRIVE FROM THE LEFT IN SECONDS This display must be angled to be seen by the stopped driver 14

33. Split-Hybrid STOP VEHICLE WILL ARRIVE FROM THE RIGHT IN SECONDS 3 VEHIC WILL ARRI FROM LEFT IN SECONDS VEHIC WILL ARR FROM LEFT IN SECONDS VEHICLE WILL ARRIVE FROM THE LEFT IN SECONDS 14 This display must be angled to be seen by the stopped driver When a vehicle is within the arrival time that defines the safe gap limit, the background changes to red and the arrival time flashes Both the left and right displays will show the same symbols.

34. Split-Hybrid STOP VEHICLE WILL ARRIVE FROM THE RIGHT IN SECONDS 3 VEHIC WILL ARRI FROM LEFT IN SECONDS VEHIC WILL ARR FROM LEFT IN SECONDS VEHICLE WILL ARRIVE FROM THE LEFT IN SECONDS This display must be angled to be seen by the stopped driver 6

35. Speedometer STOP FAST VEHICLES APPROACHING FROM LEFT MPH 55 Speed changes white and flashes; background changes red when major road vehicle approaches at greater (> 10mph) than posted speed FROM RIGHT MPH 85

36. Speedometer

37. Classification of concepts How each concept automates or supports the information processing stages of drivers at thru-STOP intersections (from Parasuraman, Sheridan, and Wickens; 2000). Information acquisition: Extent to which each concept helps with sensing and detecting info (i.e., vehicles, hazards) Low = applying limited or no sensors to scan and observe different parts of the road High = filtering and highlighting specific information content from sensors Information analysis: Extent to which information is processed and inferences made Low = predict changes in information over time High = integrate information and potentially extract a single value Decision making: Process by which decision alternatives are evaluated and selected Low = present a driver with the full set of alternatives High = make the decision for the driver and act autonomously Action execution: Process by which a specific action is completed Low = automating a simple task such as turning on the vehicle headlights High = taking full control of a car

38. Overview

39. Evaluation Simulation required Interfaces do not exist in real world Need flexibility to modify interfaces Need control over traffic (and environment) conditions Need repeated exposure to same conditions to produce reliable data Simulation limits Calibration with real world data from on site instrumentation Limitations to “size” of experiment Time intensive to implement and validate

40. Practical limits to size of experiment Keep subjects 2 to 3 hours; < 2 hrs of driving in 30 min sessions. Issues: 5 interface conditions (baseline, static warning, hazard beacon, hybrid, and speedometer). All subjects will see all condition worlds. In each world, mainline traffic conditions will be scripted to represent specific gap sequences – need to determine wait time and the presence of different (safe) gap sizes in the traffic stream. Will test long and short wait times. To collect reliable data (e.g., gap size accepted, clearance time, safety margin with respect to remaining gap during merge), each condition world must be experienced at least twice…Implies that each condition world must have at least 2 variants in terms of traffic conditions. Each replicated world will need different traffic conditions to limit effects of learning and expectancy on driver decisions. If allow 10 minutes for each drive, then we have approx 1 and 2/3 hrs of driving per subject. May be too much for individual drivers (notably older drivers). Piloting will be used to evaluate study design.

41. Conclusion Task Completed: Intersection selected and simulated with high Geospecific accuracy. Task On schedule: Interface concepts generated based on human factors analysis and preliminary review by experts. Interface candidates simulated in driving simulator environment. Demo scheduled for project panel.