Developing a Digital Highway Framework to Serve County Roads

Principal Investigator(s):

Max Donath, Professor, Mechanical Engineering

Project summary:

Roadway departure crashes represent a major road safety issue on both the national and state level. This project aimed to develop and evaluate a low-cost, vehicle-mounted sensor suite capable of generating map data with lane and road boundary information accurate to the 10 cm (4 in) level. Such a map could be used for a number of different applications including GNSS/GPS-based lane departure avoidance systems, smartphone-based dynamic curve speed warning systems, and basemap improvements, among others.

The sensor suite used consists of a high-accuracy GNSS receiver, a side-facing video camera, and a computer. Including cabling and mounting hardware, the equipment costs were roughly $30,000. Here, the side-facing camera is used to record video of the ground adjacent to the passenger side of the vehicle. The video is processed using a computer vision algorithm that locates the fog line within the video frame. Using vehicle position data (provided by GNSS) and previously collected video calibration data, the fog line is located in real-world coordinates.

The system was tested on two roads (primarily two-lane, undivided highway) for which high accuracy (<10 cm) maps were available. This offset between the reference data and the computed fog line position was generally better than 7.5 cm (3 in). The results of this work demonstrate that it is feasible to use a camera to detect the position of a road’ s fog lines, or more broadly, any other lane markings, which when integrated into a larger mobile data-collection system can provide accurate lane and road boundary information about road geometry.

Project details: