Multi Lane Detection

TL;DR

This paper presents a robust multi-lane detection method for autonomous driving using CNN backbone DLA-34 and Affinity Fields, with novel decoding techniques for efficiency and flexibility in lane count.

Contribution

It introduces a lane detection approach that does not assume a fixed number of lanes and explores new decoding methods for improved efficiency.

Findings

Achieves robust detection of various lanes.

Does not assume a fixed number of lanes.

Proposes more efficient decoding methods.

Abstract

Lane detection is a long-standing task and a basic module in autonomous driving. The task is to detect the lane of the current driving road, and provide relevant information such as the ID, direction, curvature, width, length, with visualization. Our work is based on CNN backbone DLA-34, along with Affinity Fields, aims to achieve robust detection of various lanes without assuming the number of lanes. Besides, we investigate novel decoding methods to achieve more efficient lane detection algorithm.