# Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1903.02193 · HTML: https://mlsanity.com/paper/1903.02193/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 30 of 41)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [Laneformer: Object-aware Row-Column Transformers for Lane Detection](https://mlsanity.com/paper/2203.09830/) (2022)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [LaneCPP: Continuous 3D Lane Detection using Physical Priors](https://mlsanity.com/paper/2406.08381/) (2024)
- [End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving](https://mlsanity.com/paper/2102.04738/) (2021)
- [HFT: Lifting Perspective Representations via Hybrid Feature Transformation](https://mlsanity.com/paper/2204.05068/) (2022)
- [Recursive Video Lane Detection](https://mlsanity.com/paper/2308.11106/) (2023)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [A novel multimodal fusion network based on a joint coding model for lane line segmentation](https://mlsanity.com/paper/2103.11114/) (2021)
- [End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering](https://mlsanity.com/paper/2103.00345/) (2021)
- [A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection](https://mlsanity.com/paper/2110.04079/) (2021)
- [Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss](https://mlsanity.com/paper/2305.17271/) (2023)
- [Monocular BEV Perception of Road Scenes via Front-to-Top View Projection](https://mlsanity.com/paper/2211.08144/) (2022)
- [OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection](https://mlsanity.com/paper/2408.07486/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection](https://mlsanity.com/paper/2601.04968/) (2026)
- [SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection](https://mlsanity.com/paper/2308.12863/) (2023)
- [Federated Adversarial Learning for Robust Autonomous Landing Runway Detection](https://mlsanity.com/paper/2406.15925/) (2024)
- [LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation](https://mlsanity.com/paper/2408.13852/) (2024)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [Learning robust driving policies without online exploration](https://mlsanity.com/paper/2103.08070/) (2021)
- [Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles](https://mlsanity.com/paper/2209.07042/) (2022)
- [Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification](https://mlsanity.com/paper/2211.04165/) (2022)
- [Contrastive Learning for Lane Detection via cross-similarity](https://mlsanity.com/paper/2308.08242/) (2023)
- [Composition and Application of Current Advanced Driving Assistance System: A Review](https://mlsanity.com/paper/2105.12348/) (2021)
- [Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation](https://mlsanity.com/paper/2202.05204/) (2022)
- [RONELDv2: A faster, improved lane tracking method](https://mlsanity.com/paper/2202.13137/) (2022)
- [Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction](https://mlsanity.com/paper/2306.04947/) (2023)
- [LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves](https://mlsanity.com/paper/2403.05155/) (2024)

## Abstract (arXiv)

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.