  Building a fully autonomous self-driving system has been discussed for more
than 20 years yet remains unsolved. Previous systems have limited ability to
scale. Their localization subsystem needs labor-intensive map recording for
running in a new area, and the accuracy decreases after the changes occur in
the environment. In this paper, a new localization method is proposed to solve
the scalability problems, with a new method for detecting and making sense of
diverse traffic lines. Like the way human drives, a self-driving system should
not rely on an exact position to travel in most scenarios. As a result, without
HD Maps, GPS or IMU, the proposed localization subsystem relies only on
detecting driving-related features around (like lane lines, stop lines, and
merging lane lines). For spotting and reasoning all these features, a new line
detector is proposed and tested against multiple datasets.
