Main road pavement stakeholders, the European Commission (EC), The Federal Highway Administration (FHWA),US Department of Transport ( USDOT) and Road Safety Associations European Road Federation (ERF) & American Traffic Safety Services Association (ATSSA), all recognize and support that good quality and uniformity of road markings, signage, and other traffic control devices support safe and efficient driving by both human drivers and automated and autonomous vehicles.

RetroTek attached to front of vehicle

Intel Movidius Neural Compute Stick

The RetroTek team in Ireland are working on a project ‘AI Classifying Road Markings‘ using the Intel Movidius Neural Compute Stick technology which allows the RetroTek technology to utilize and deploy deep neural network and computer vision applications. Thus, providing cutting edge solutions for deploying deep learning and computer vision algorithms on the RetroTek device at ultra-low power. This project will enable the RetroTek Road Marking Retroreflectometer Technology to Identify and Classify the Quality of Pavement Road Marking using Computational Neural Networks (CNNs).

The RetroTek AI-assisted technology enables the classification of the quality of road markings day & night which was mentioned as a key requirement in the CEDR Premium Project for the Conference of European Directors of Roads (CEDR) to assist in maintaining safe roads i.e.

  • Identification & classification of the type /shape of the pavement marking.
  • Quantify the paint/thermoplastic wear and coverage patterns of these markings (lines, arrows, symbols and text messages) that have been damaged by traffic & weather. Good quality visual coverage is required for day time vehicle operation.
  • Quantify Measurement of the amount of retroreflective bead material coverage that on the markings which is essential for marking night visibility and safe road use at night.

Road markings, when heavily damaged or worn away, cannot be classified using conventional machine vision techniques. This development will assist road pavement authorities and maintenance contractors to assess the day and night visibility performance of pavement marking conditions and to predict and plan pavement marking maintenance where specifically required.

Pavement Marking Samples.

Demonstrating Wear quality and Retroreflectivity coverage.

Good Quality Marking(100% visible)

Poor Quality -Worn with Low (25% coverage) Retroreflectivity Coverage

Poor Quality with 5% visible Marking Coverage and 0% Retroreflectivity Coverage

Many global research projects indicate that maintaining good quality road pavement markings is important to ensure that vehicles driven by humans and equipped with automated features such as Advanced Driver Assist Systems (ADAS) and Autonomous vehicles can all operate safely on our road networks.

The requirement for quality, reliable and affordable pavement quality assessment to industry standards has never been greater and the RetroTek Technologies are ready to meet that challenge. RetroTek Retroreflectometers are the safe, affordable and efficient pavement marking assessment solution. Mounted to the front of a vehicle, travelling at all traffic speeds, safely and efficiently assess simultaneously the day contrast and night visibility (retroreflectivity) of edge and centre line markings, centre lane markings/symbols all in the one pass.

References:

  • RetroTek Technology. Reflective Measurement Systems Ltd.
  • Intel Movidius Neural Compute Stick
  • ERF technical recommendations to adapt CAVs – Road Markings
  • European Commission agreement on the revised General Safety Regulations
  • FHWA announced their intention to release a 2020 update to the Manual on Uniform Traffic Control Devices (MUTCD).
  • The USDOT policy was further elaborated with the publication of “USDOT – Preparing for the future of Transportation – Automated Vehicles 3.0”.
  • ATSSA have also adopted a Policy on Road Markings for Machine Vision Systems to enable safe and efficient operations of ADS” (Automated Driving Systems).
  • CEDR PREMiUM Project