Using DP Towards A Shortest Path Problem-Related Application

TL;DR

This paper introduces a dynamic programming-based directed graph model to detect curved lanes in autonomous driving, formulating it as a shortest path problem and achieving high detection accuracy on KITTI data.

Contribution

The paper presents a novel graph model and algorithm that effectively detect curved lanes by solving a shortest path problem using dynamic programming.

Findings

Achieved 97.5% average detection precision on KITTI dataset.

Successfully modeled lane detection as a shortest path problem.

Demonstrated effectiveness of the approach on real-world sequences.

Abstract

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%.