Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving

• URL: http://arxiv.org/abs/2509.22756v1
• Date: Fri, 26 Sep 2025 09:33:36 GMT
• Title: Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
• Authors: Shiyi Liang, Xinyuan Chang, Changjie Wu, Huiyuan Yan, Yifan Bai, Xinran Liu, Hang Zhang, Yujian Yuan, Shuang Zeng, Mu Xu, Xing Wei,
• Abstract summary: PAMR (Persistent Autoregressive Mapping with Traffic Rules) is a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations.<n>Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency.
• Score: 20.07038318208438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations. Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency across these segments. To properly evaluate continuous and consistent map generation, we develop MapDRv2, featuring improved lane geometry annotations. Extensive experiments demonstrate that PAMR achieves superior performance in joint vector-rule mapping tasks, while maintaining persistent rule effectiveness throughout extended driving sequences.

Related papers

• SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving [52.02379432801349]
  We propose SGDrive, a novel framework that structures the VLM's representation learning around driving-specific knowledge hierarchies.<n>Built upon a pre-trained VLM backbone, SGDrive decomposes driving understanding into a scene-agent-goal hierarchy that mirrors human driving cognition.
  arXiv  Detail & Related papers  (2026-01-09T08:55:42Z)
• Unsupervised Monocular Road Segmentation for Autonomous Driving via Scene Geometry [2.8647133890966994]
  This paper presents a fully unsupervised approach for binary road segmentation (road vs. non-road)<n>The method leverages scene geometry and temporal cues to distinguish road from non-road regions.<n>On the Cityscapes dataset, the model achieves an Intersection-over-Union (IoU) of 0.82, demonstrating high accuracy with a simple design.
  arXiv  Detail & Related papers  (2025-10-19T10:59:43Z)
• Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps [26.036008442130587]
  Most autonomous cars rely on the availability of high-definition (HD) maps.<n>Current research aims to address this constraint by directly predicting HD map elements from onboard sensors.<n>We propose a coherent approach to predict lane segments and their corresponding topology, as well as road boundaries.
  arXiv  Detail & Related papers  (2025-07-02T06:26:17Z)
• TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving [52.25176274203747]
  TopoStreamer is an end-to-end temporal perception model for lane segment topology reasoning.<n>TopoStreamer introduces three key improvements: streaming attribute constraints, dynamic lane boundary positional encoding, and lane segment denoising.<n>On the Open-Lane-V2 dataset, TopoStreamer demonstrates significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-01T12:10:46Z)
• Inferring Driving Maps by Deep Learning-based Trail Map Extraction [5.381140012327021]
  High-definition (HD) maps offer extensive and accurate environmental information about the driving scene.<n>Recent trends have moved from offline mapping to online mapping, ensuring availability and actuality of the utilized maps.<n>We propose a novel offline mapping approach that integrates trails - informal routes used by drivers - into the map creation process.
  arXiv  Detail & Related papers  (2025-05-15T13:09:19Z)
• Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map [15.57801519192153]
  We introduce MapDR, a dataset designed for the extraction of Driving Rules from traffic signs.<n>MapDR features over $10,000$ annotated video clips that capture the intricate correlation between traffic sign regulations and lanes.<n>It fills a critical gap in the integration of traffic sign rules, contributing to the development of reliable autonomous driving systems.
  arXiv  Detail & Related papers  (2024-10-31T09:53:21Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories [7.5390770178143045]
  This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.
  arXiv  Detail & Related papers  (2023-01-22T20:16:12Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• DAGMapper: Learning to Map by Discovering Lane Topology [84.12949740822117]
  We focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges. We formulate the problem as inference in a directed acyclic graphical model (DAG), where the nodes of the graph encode geometric and topological properties of the local regions of the lane boundaries. We show the effectiveness of our approach on two major North American Highways in two different states and show high precision and recall as well as 89% correct topology.
  arXiv  Detail & Related papers  (2020-12-22T21:58:57Z)
• Learning Lane Graph Representations for Motion Forecasting [92.88572392790623]
  We construct a lane graph from raw map data to preserve the map structure. We exploit a fusion network consisting of four types of interactions, actor-to-lane, lane-to-lane, lane-to-actor and actor-to-actor. Our approach significantly outperforms the state-of-the-art on the large scale Argoverse motion forecasting benchmark.
  arXiv  Detail & Related papers  (2020-07-27T17:59:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.