Visibility of various road markings for machine vision

•Road markings with various properties were tested for machine vision recognition.•Good recognition was measured under dry conditions with both cameras and LiDAR.•Under conditions of moisture, highly retroreflective markings were superior.•Premium glass beads in structured markings gave performance comparable to tape.•Good correlation was found between road marking retroreflectivity and detection. The same features of road markings – retroreflectivity and daytime visibility – are the key parameters for their recognition by both human drivers and for machine vision (MV) utilised by the emerging technology of automated vehicles. For the purpose of a side-by-side performance assessment of various road markings, 8 materials, differing in colour and retroreflectivity, were tested under laboratory conditions for visibility by LiDAR and by cameras under different weather conditions. Visibility was evaluated under various intensities of rain and fog; simulated effects of glare from oncoming vehicle were also tested. The response of MV equipment depended on (1) the equipment itself, (2) retroreflectivity of road marking, (3) their structure, (4) their colour, and (5) the utilised glass beads. Overall, the highest MV intensities were measured with structured cold plastic reflectorised with ‘premium’ glass beads (refractive index 1.6–1.7) and with white road marking tape. Unexpectedly, orange paint under dry conditions furnished LiDAR recognition disproportionally high to its retroreflectivity. Poorest outcome gave greyish paint imitating severely worn markings. During rain and during fog, use of the ‘premium’ glass beads resulted in significantly improved camera contrast ratio, but there was no such correlation with LiDAR intensity. On average, introduction of moisture lowered the measured contrast ratio by 80 % (range 69 %–86 %) and LiDAR response intensity by 84 % (range 72 %–96 %). Results from this case study can be used for development of road marking materials with improved recognition by MV.