Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention

TL;DR

This paper presents a real-time, vision-only method using recurrent neural networks to accurately estimate the current lane ID in multi-lane roads, improving efficiency and robustness over existing approaches.

Contribution

It introduces a novel end-to-end monocular camera-based solution with a dual convention, leveraging temporal data to enhance lane ID classification accuracy.

Findings

Achieves over 95% accuracy on challenging datasets

Operates in real-time with low computational complexity

Outperforms high-complexity state-of-the-art models

Abstract

Acquiring information about the road lane structure is a crucial step for autonomous navigation. To this end, several approaches tackle this task from different perspectives such as lane marking detection or semantic lane segmentation. However, to the best of our knowledge, there is yet no purely vision based end-to-end solution to answer the precise question: How to estimate the relative number or "ID" of the current driven lane within a multi-lane road or a highway? In this work, we propose a real-time, vision-only (i.e. monocular camera) solution to the problem based on a dual left-right convention. We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight. Our approach is designed to meet low-complexity specifications and limited runtime requirements. It harnesses the temporal dimension inherent to the input sequences to improve upon high-complexity state-of-the-art models. We achieve more than 95% accuracy on a challenging test set with extreme conditions and different routes.