# Heatmap-based Vanishing Point boosts Lane Detection

arXiv: https://arxiv.org/abs/2007.15602 · HTML: https://mlsanity.com/paper/2007.15602/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions](https://mlsanity.com/paper/2406.00934/) (2024)
- [End-to-End Monocular Vanishing Point Detection Exploiting Lane Annotations](https://mlsanity.com/paper/2108.13699/) (2021)
- [Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective](https://mlsanity.com/paper/2607.05783/) (2026)

## Abstract (arXiv)

Vision-based lane detection (LD) is a key part of autonomous driving technology, and it is also a challenging problem. As one of the important constraints of scene composition, vanishing point (VP) may provide a useful clue for lane detection. In this paper, we proposed a new multi-task fusion network architecture for high-precision lane detection. Firstly, the ERFNet was used as the backbone to extract the hierarchical features of the road image. Then, the lanes were detected using image segmentation. Finally, combining the output of lane detection and the hierarchical features extracted by the backbone, the lane VP was predicted using heatmap regression. The proposed fusion strategy was tested using the public CULane dataset. The experimental results suggest that the lane detection accuracy of our method outperforms those of state-of-the-art (SOTA) methods.