Multi-lane Detection Using Instance Segmentation and Attentive Voting

TL;DR

This paper introduces a novel multi-lane detection method using instance segmentation and attentive voting, achieving high accuracy and speed, and provides a new dataset with intuitive labels for autonomous driving applications.

Contribution

The paper presents a new multi-lane detection approach that outperforms existing methods in accuracy and speed, along with a new dataset with improved labeling scheme.

Findings

Lane segmentation accuracy of 99.87%

Processing speed of 54.53 fps

Outperforms state-of-the-art methods in accuracy and speed

Abstract

Autonomous driving is becoming one of the leading industrial research areas. Therefore many automobile companies are coming up with semi to fully autonomous driving solutions. Among these solutions, lane detection is one of the vital driver-assist features that play a crucial role in the decision-making process of the autonomous vehicle. A variety of solutions have been proposed to detect lanes on the road, which ranges from using hand-crafted features to the state-of-the-art end-to-end trainable deep learning architectures. Most of these architectures are trained in a traffic constrained environment. In this paper, we propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. To achieve this, we also offer a dataset with a more intuitive labeling scheme as compared to other benchmark datasets. Using our approach, we are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average).