# Lane Detection and Classification using Cascaded CNNs

arXiv: https://arxiv.org/abs/1907.01294 · HTML: https://mlsanity.com/paper/1907.01294/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [Structure Guided Lane Detection](https://mlsanity.com/paper/2105.05403/) (2021)
- [Laneformer: Object-aware Row-Column Transformers for Lane Detection](https://mlsanity.com/paper/2203.09830/) (2022)
- [Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving](https://mlsanity.com/paper/2202.07133/) (2022)
- [LaneCPP: Continuous 3D Lane Detection using Physical Priors](https://mlsanity.com/paper/2406.08381/) (2024)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection](https://mlsanity.com/paper/2601.04968/) (2026)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [Repainting and Imitating Learning for Lane Detection](https://mlsanity.com/paper/2210.05097/) (2022)
- [LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems](https://mlsanity.com/paper/2408.09117/) (2024)
- [Multi Lane Detection](https://mlsanity.com/paper/2212.11533/) (2022)
- [LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions](https://mlsanity.com/paper/2307.06853/) (2023)
- [HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation](https://mlsanity.com/paper/2603.10128/) (2026)
- [HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection](https://mlsanity.com/paper/2606.31172/) (2026)

## Abstract (arXiv)

Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.