Road Markings for Machine Vision NCHRP Project (6) Update to AASHTO SCOTE June 2016
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1 Road Markings for Machine Vision NCHRP Project (6) Update to AASHTO SCOTE June 2016
2 NCHRP Project Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies (01) Policy and Planning Actions to Internalize Societal Impacts of CV and AV Systems into Market Decisions (02) Impacts of Regulations and Policies on CV and AV Technology Introduction in Transit Operations (03) Challenges to CV and AV Application in Truck Freight Operations (05) Strategic Communications Plan for NCHRP Project (06) Road Markings for Machine Vision (07) Implications of Automation for Motor Vehicle Codes (08) Dedicating Lanes for Priority or Exclusive Use by CVs and AVs (09) Providing Support to the Introduction of CV/AV Impacts into Regional Transportation Planning and Modeling Tools
3 Cameras to Detect Markings
4 Adaptive Headlights Potential Benefit Demonstrated Benefit
5 Lane Departure Warning Potential Benefit Demonstrated Benefit
6 Highway Safety Statistics Roadway departure crashes account for over half of all fatal crashes (51%)
7 Potential LDW Safety Decrease fatalities by 7,529 fatal per year in the US Journal of Accident Analysis & Prevention, Vol 43, 2011 Reduce roadway departure crashes 26.1% NHTSA Reduce serious injuries 20.7% NHTSA
8 LDW Experiences Not effective as other vehicle safety technologies (IIHS 2014) we don t see any evidence that these systems are helping drivers avoid being in crashes (IIHS 2014) 2/3 of Honda owners turn off their LDW feature (IIHS 2016) Annoying warnings
9 Early TTI Studies Simulated Conditions Sun glare Wet conditions Flat v. structured markings
10 Machine Vision video
11 Challenging Conditions Uniform road marking maintenance thresholds Preventive pavement maintenance treatments Horizontal curves Roadway lighting Nighttime conditions Wet conditions Snow conditions Debris Poor marking removal
12 Historic Coordination Vehicle Industry Highway Infrastructure Industry Standards Standards
13 Needed Coordination Vehicle Industry (NHTSA, SAE) Highway Infrastructure Industry (FHWA, AASHTO) Standards
14 NCHRP (6) Road Markings for Machine Vision Objectives develop information on the performance characteristics of pavement markings that affect the ability of machine vision systems to recognize them provide data and recommendations that the AASHTO/SAE Working Group can use to quickly develop guidelines and criteria
15 Work Plan Kick-Off Meeting Review Policies & Machine Vision Technologies Identify Testing Conditions Conduct Closed-Course Testing Analyze Results Prepare Reports
16 Current Testing Requirements ISO 17361:2007 No requirements on types of road markings Lane markings must be in good condition and in accordance with the nationally defined visible lane markings std No requirements on the environmental conditions Visibility range must be greater than 1 km NHTSA High contrast and uniform pavement Lane marking specifications adhering to MUTCD Avoiding tests in inclement weather including rain, fog, snow, hail, smoke, or ash
17 Field Data
18
19
20 Texas A&M RELLIS Campus
21 Markings (Level 1)
22 Markings (Level 2)
23 Markings (Level 3)
24 Markings (Level 4)
25 Markings (Level 5)
26 Test Markings Possible Markings Continuous white Continuous yellow Skip white Skip yellow Contrast markings Raised retroreflective pavement markers Raised non-retroreflective pavement markers
27 Test Conditions Daytime Weather (dry, wet) Nighttime Conditions (dry, wet) Continuous roadway lighting (on or off) Vehicle lighting (tungsten-halogen, LED, other)
28 Next Steps Identify and obtain vehicle industry data Synthesize all data sources Develop up to 5 marking levels Color and retroreflectivity Provide 3M and Ennis-Flint specifications Install markings at RELLIS Conduct testing
29
30 Sensor-Enhanced Markings Pilot testing of enhanced detection technologies
31 Making It Happen
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