A Dataset for Lane Instance Segmentation in Urban Environments

TL;DR

This paper introduces a semi-automated labeling method for lane instance segmentation in urban environments, significantly reducing annotation time and providing a new dataset for autonomous vehicle research.

Contribution

It presents a novel semi-automated labeling approach using 3D road plane estimation and releases a large dataset for lane instance segmentation.

Findings

Average labeling time per image reduced to 5 seconds

Dataset of 24,000 images released

Experimental results on semantic and instance segmentation included

Abstract

Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the time-consuming manual labelling process, typically applied per image. We notice that driving the car is itself a form of annotation. Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence. The average labelling time per image is reduced to 5 seconds and only an inexpensive dash-cam is required for data capture. We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.