# Holistic Grid Fusion Based Stop Line Estimation

arXiv: https://arxiv.org/abs/2009.09093 · HTML: https://mlsanity.com/paper/2009.09093/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [ROMNet: Renovate the Old Memories](https://mlsanity.com/paper/2202.02606/) (2022)
- [Accurate non-stationary short-term traffic flow prediction method](https://mlsanity.com/paper/2205.00517/) (2022)

## Abstract (arXiv)

Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufficient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.