  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.
