# Inferring Driving Maps by Deep Learning-based Trail Map Extraction

arXiv: https://arxiv.org/abs/2505.10258 · HTML: https://mlsanity.com/paper/2505.10258/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 30 of 31)

- [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)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (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)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction](https://mlsanity.com/paper/2308.12570/) (2023)
- [Neural Map Prior for Autonomous Driving](https://mlsanity.com/paper/2304.08481/) (2023)
- [MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping](https://mlsanity.com/paper/2403.15951/) (2024)
- [FocalFormer3D : Focusing on Hard Instance for 3D Object Detection](https://mlsanity.com/paper/2308.04556/) (2023)
- [P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors](https://mlsanity.com/paper/2403.10521/) (2024)
- [MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2404.00876/) (2024)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction](https://mlsanity.com/paper/2402.17430/) (2024)
- [Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It](https://mlsanity.com/paper/2312.06420/) (2023)
- [Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors](https://mlsanity.com/paper/2406.03105/) (2024)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving](https://mlsanity.com/paper/2305.04205/) (2023)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [PriorMapNet: Enhancing Online Vectorized HD Map Construction with Priors](https://mlsanity.com/paper/2408.08802/) (2024)
- [MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2409.18737/) (2024)
- [Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey](https://mlsanity.com/paper/2304.10891/) (2023)
- [Towards a Functional System Architecture for Automated Vehicles](https://mlsanity.com/paper/1703.08557/) (2017)
- [Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants](https://mlsanity.com/paper/1906.02495/) (2019)
- [FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow](https://mlsanity.com/paper/2305.01622/) (2023)
- [LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations](https://mlsanity.com/paper/2409.12409/) (2024)

## Built on by (most cited first, 2 of 2)

- [D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction](https://mlsanity.com/paper/2606.20725/) (2026)
- [ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data](https://mlsanity.com/paper/2604.24353/) (2026)

## Abstract (arXiv)

High-definition (HD) maps offer extensive and accurate environmental information about the driving scene, making them a crucial and essential element for planning within autonomous driving systems. To avoid extensive efforts from manual labeling, methods for automating the map creation have emerged. Recent trends have moved from offline mapping to online mapping, ensuring availability and actuality of the utilized maps. While the performance has increased in recent years, online mapping still faces challenges regarding temporal consistency, sensor occlusion, runtime, and generalization. We propose a novel offline mapping approach that integrates trails - informal routes used by drivers - into the map creation process. Our method aggregates trail data from the ego vehicle and other traffic participants to construct a comprehensive global map using transformer-based deep learning models. Unlike traditional offline mapping, our approach enables continuous updates while remaining sensor-agnostic, facilitating efficient data transfer. Our method demonstrates superior performance compared to state-of-the-art online mapping approaches, achieving improved generalization to previously unseen environments and sensor configurations. We validate our approach on two benchmark datasets, highlighting its robustness and applicability in autonomous driving systems.