  Autonomous detection of lane markers improves road safety, and purely visual
tracking is desirable for widespread vehicle compatibility and reducing sensor
intrusion, cost, and energy consumption. However, visual approaches are often
ineffective because of a number of factors; e.g., occlusion, poor weather
conditions, and paint wear-off. We present an approach to enhance lane marker
appearance for assisted and autonomous driving, particularly under poor
visibility. Our method, named SafeDrive, attempts to improve visual lane
detection approaches in drastically degraded visual conditions. SafeDrive finds
lane markers in alternate imagery of the road at the vehicle's location and
reconstructs a sparse 3D model of the surroundings. By estimating the geometric
relationship between this 3D model and the current view, the lane markers are
projected onto the visual scene; any lane detection algorithm can be
subsequently used to detect lanes in the resulting image. SafeDrive does not
require additional sensors other than vision and location data. We demonstrate
the effectiveness of our approach on a number of test cases obtained from
actual driving data recorded in urban settings.
