  The detection of curved lanes is still challenging for autonomous driving
systems. Although current cutting-edge approaches have performed well in real
applications, most of them are based on strict model assumptions. Similar to
other visual recognition tasks, lane detection can be formulated as a
two-dimensional graph searching problem, which can be solved by finding several
optimal paths along with line segments and boundaries. In this paper, we
present a directed graph model, in which dynamic programming is used to deal
with a specific shortest path problem. This model is particularly suitable to
represent objects with long continuous shape structure, e.g., lanes and roads.
We apply the designed model and proposed an algorithm for detecting lanes by
formulating it as the shortest path problem. To evaluate the performance of our
proposed algorithm, we tested five sequences (including 1573 frames) from the
KITTI database. The results showed that our method achieves an average
successful detection precision of 97.5%.
