Title: SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility,
Abstract: We present an approach towards robust lane tracking for assisted and
autonomous driving, particularly under poor visibility. 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, including but not limited to occlusion, poor weather
conditions, and paint wear-off. Our method, named SafeDrive, attempts to
improve visual lane detection approaches in drastically degraded visual
conditions without relying on additional active sensors. In scenarios where
visual lane detection algorithms are unable to detect lane markers, the
proposed approach uses location information of the vehicle to locate and access
alternate imagery of the road and attempts detection on this secondary image.
Subsequently, by using a combination of feature-based and pixel-based
alignment, an estimated location of the lane marker is found in the current
scene. We demonstrate the effectiveness of our system on actual driving data
from locations in the United States with Google Street View as the source of
alternate imagery.