Computer Vision in Highway Transportation Research

Highway transportation research is collecting and analyzing an increasing amount of video data. Reasons include recent technological breakthroughs that provide new and enhanced visual and other sensors for conducting research in multiple areas such as system planning, operations, safety, and infrastructure condition assessment.

• Eye tracking technology now allows for more natural behavior and field applications. Examples of research using this technology include the Exploratory Advanced Research (EAR) Program-sponsored research projects, “Seeing in the Dark Improving Understanding of Driver Visibility Requirements at Night,” [more] and “Development of Methodologies to Evaluate the Nighttime Safety Implications of the Roadway Visual Scene Under Varying Cognitive Task Loads.” [more]


• Increased use of traffic cameras and improved analysis software are providing data on vehicle classification, weight, speed, routing. Road-based and vehicle-based cameras also are providing improved recognition of pedestrians, e.g. the EAR Program-sponsored project, “Real–Time Pedestrian Detection Layered Object Recognition System for Pedestrian Collision Sensing.” [more]


• Use of LIDAR and improved analysis software are assisting with the cataloguing of road and roadside conditions including pavement surface conditions, roadside topography, and roadside hardware.

The Strategic Highway Research Program (SHRP) 2 safety area includes elements of the above types of data in the naturalistic driving study. The amount of data can be orders of magnitude larger than what the highway research has worked with previously with research results expected to exceed one petabyte. See http://www.trb.org/StrategicHighwayResearchProgram2SHRP2/Public/Pages/Safety_153.aspx, for more information on the SHRP2 safety area.

While the research community is fortunate to be able to collect more and better data, the amount of data has the potential to overwhelm the capacity to assess the data using current methods. Current methods include a mix of automated and manual, frame-by-frame coding that is not able to manage massive data stream and provides results that are not consistent or error free enough.

ITS America conducted a technology scan for the USDOT Intelligent Transportation Systems (ITS) Joint Program Office. The scan report, "Connected Vehicles: Trends in Computer Vision," are located at http://www.itsa.org/knowledgecenter/technologyscan. As the technology moves from stand alone to integrated systems and from systems that provide driver or operator warnings to direct control of vehicle and infrastructure systems (i.e. steering, acceleration, braking, taffic signals) there is a critical need for compatable systems architecture and common conditions against which multiple manufacturers can test their equipment and software.