# Topology Preserving Local Road Network Estimation from Single Onboard Camera Image

arXiv: https://arxiv.org/abs/2112.10155 · HTML: https://mlsanity.com/paper/2112.10155/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 44 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 30 of 36)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Cross-view Semantic Segmentation for Sensing Surroundings](https://mlsanity.com/paper/1906.03560/) (2019)
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://mlsanity.com/paper/1803.09693/) (2018)
- [Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://mlsanity.com/paper/1906.08945/) (2019)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps](https://mlsanity.com/paper/2003.06754/) (2020)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [HDNET: Exploiting HD Maps for 3D Object Detection](https://mlsanity.com/paper/2012.11704/) (2020)
- [Key Points Estimation and Point Instance Segmentation Approach for Lane Detection](https://mlsanity.com/paper/2002.06604/) (2020)
- [FISHING Net: Future Inference of Semantic Heatmaps In Grids](https://mlsanity.com/paper/2006.09917/) (2020)
- [MonoLayout: Amodal scene layout from a single image](https://mlsanity.com/paper/2002.08394/) (2020)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [Hierarchical Recurrent Attention Networks for Structured Online Maps](https://mlsanity.com/paper/2012.12314/) (2020)
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://mlsanity.com/paper/1902.00293/) (2019)
- [Convolutional Recurrent Network for Road Boundary Extraction](https://mlsanity.com/paper/2012.12160/) (2020)
- [3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation](https://mlsanity.com/paper/2011.01535/) (2020)
- [Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization](https://mlsanity.com/paper/1908.03274/) (2019)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)

## Built on by (most cited first, 30 of 34)

- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving](https://mlsanity.com/paper/2312.16108/) (2023)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning](https://mlsanity.com/paper/2310.06753/) (2023)
- [T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving](https://mlsanity.com/paper/2411.18894/) (2024)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes](https://mlsanity.com/paper/2405.14747/) (2024)
- [Enhancing Vectorized Map Perception with Historical Rasterized Maps](https://mlsanity.com/paper/2409.00620/) (2024)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping](https://mlsanity.com/paper/2308.08543/) (2023)
- [RoadPainter: Points Are Ideal Navigators for Topology transformER](https://mlsanity.com/paper/2407.15349/) (2024)
- [CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation](https://mlsanity.com/paper/2209.07734/) (2022)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving](https://mlsanity.com/paper/2505.17771/) (2025)
- [Improving Online Lane Graph Extraction by Object-Lane Clustering](https://mlsanity.com/paper/2307.10947/) (2023)
- [LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement](https://mlsanity.com/paper/2401.17609/) (2024)
- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [Mind the map! Accounting for existing map information when estimating online HDMaps from sensor](https://mlsanity.com/paper/2311.10517/) (2023)
- [LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation](https://mlsanity.com/paper/2511.06272/) (2025)
- [TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture](https://mlsanity.com/paper/2306.05419/) (2023)
- [Lane Graph Extraction from Aerial Imagery via Lane Segmentation Refinement with Diffusion Models](https://mlsanity.com/paper/2405.00620/) (2024)
- [Continuity Preserving Online CenterLine Graph Learning](https://mlsanity.com/paper/2407.11337/) (2024)
- [Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors](https://mlsanity.com/paper/2411.17161/) (2024)
- [GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction](https://mlsanity.com/paper/2409.08688/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [Translating Images to Road Network: A Sequence-to-Sequence Perspective](https://mlsanity.com/paper/2402.08207/) (2024)
- [Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes](https://mlsanity.com/paper/2506.13553/) (2025)
- [URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments](https://mlsanity.com/paper/2309.08814/) (2023)
- [A Concise Survey on Lane Topology Reasoning for HD Mapping](https://mlsanity.com/paper/2504.01989/) (2025)

## Abstract (arXiv)

Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground plane, where also the driving actions are taken. This paper aims at extracting the local road network topology, directly in the bird's-eye-view (BEV), all in a complex urban setting. The only input consists of a single onboard, forward looking camera image. We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points. To better capture topology, we introduce the concept of minimal cycles and their covers. A minimal cycle is the smallest cycle formed by the directed curve segments (between two intersections). The cover is a set of curves whose segments are involved in forming a minimal cycle. We first show that the covers suffice to uniquely represent the road topology. The covers are then used to supervise deep neural networks, along with the lane curve supervision. These learn to predict the road topology from a single input image. The results on the NuScenes and Argoverse benchmarks are significantly better than those obtained with baselines. Code: https://github.com/ybarancan/TopologicalLaneGraph