# SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions

arXiv: https://arxiv.org/abs/2507.04822 · HTML: https://mlsanity.com/paper/2507.04822/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 33 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping](https://mlsanity.com/paper/2304.10440/) (2023)
- [ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe](https://mlsanity.com/paper/2312.17133/) (2023)
- [Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps](https://mlsanity.com/paper/2311.04079/) (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)
- [HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction](https://mlsanity.com/paper/2403.08639/) (2024)
- [LATR: 3D Lane Detection from Monocular Images with Transformer](https://mlsanity.com/paper/2308.04583/) (2023)
- [CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention](https://mlsanity.com/paper/2209.07989/) (2022)
- [Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?](https://mlsanity.com/paper/2309.16595/) (2023)
- [GNN-LM: Language Modeling based on Global Contexts via GNN](https://mlsanity.com/paper/2110.08743/) (2021)
- [LaneCPP: Continuous 3D Lane Detection using Physical Priors](https://mlsanity.com/paper/2406.08381/) (2024)
- [GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping](https://mlsanity.com/paper/2307.09472/) (2023)
- [Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map](https://mlsanity.com/paper/2410.23780/) (2024)
- [LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement](https://mlsanity.com/paper/2401.17609/) (2024)
- [RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception](https://mlsanity.com/paper/2407.10876/) (2024)

## Built on by (most cited first, 10 of 10)

- [JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation](https://mlsanity.com/paper/2509.22548/) (2025)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving](https://mlsanity.com/paper/2509.22756/) (2025)
- [EagleVision: A Dual-Stage Framework with BEV-grounding-based Chain-of-Thought for Spatial Intelligence](https://mlsanity.com/paper/2512.15160/) (2025)
- [AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction](https://mlsanity.com/paper/2512.19150/) (2025)
- [Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps](https://mlsanity.com/paper/2507.07487/) (2025)
- [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025)
- [Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis](https://mlsanity.com/paper/2603.12997/) (2026)
- [Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior](https://mlsanity.com/paper/2606.31814/) (2026)

## Abstract (arXiv)

Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures-such as loops and bidirectional lanes-prevalent in real-world road structure. We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions, inspired by human map-drawing processes. Representing the lane graph as a directed graph G=(V,E), with intersections (V) and centerlines (E), SeqGrowGraph incrementally constructs this graph by introducing one vertex at a time. At each step, an adjacency matrix (A) expands from n × n to (n+1) × (n+1) to encode connectivity, while a geometric matrix (M) captures centerline shapes as quadratic B'ezier curves. The graph is serialized into sequences, enabling a transformer model to autoregressively predict the chain of expansions, guided by a depth-first search ordering. Evaluated on nuScenes and Argoverse 2 datasets, SeqGrowGraph achieves state-of-the-art performance.