Effective and Low-cost Lane-based Map Localization for Vehicle-Centric Route Generation
- URL: http://arxiv.org/abs/2606.16101v1
- Date: Mon, 15 Jun 2026 01:33:48 GMT
- Title: Effective and Low-cost Lane-based Map Localization for Vehicle-Centric Route Generation
- Abstract summary: OLRA is a low-cost, map-localization-based framework that derives driver-view-aligned routes by matching map-based navigation routes with camera-detected lane markings.<n>Results show OLRA outperforms OpenPilot in complex road segments and in route estimation at distance beyond 20 meters.
- Score: 3.302211996553059
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Driver-centric route representation plays a vital role in intuitive driving guidance systems. This paper presents OLRA, a low-cost, map-localization-based framework that derives driver-view-aligned routes by matching map-based navigation routes with camera-detected lane markings. This alignment process mutually enhances vehicle localization accuracy and visual route consistency. To bridge the evaluation gap across different paradigms, we introduce practical route evaluation metrics and benchmark OLRA against OpenPilot, a representative direct-generation approach. Experimental results on the nuScenes dataset demonstrate that OLRA outperforms OpenPilot in complex road segments and in route estimation at distance beyond 20 meters, achieving lower overall Euclidean error. This study is expected to promote future research in low-cost, maplocalization-based route generation methods.
Related papers
- AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
arXiv Detail & Related papers (2026-07-13T16:00:41Z) - ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data [1.5146068448101744]
One promising approach is the use of crowdsourced data from a vehicle fleet, representing road topology and lane-level features.<n>We adopt a Detection Transformer (DETR)-based approach, where a tileized representation of vehicle trajectories is used as input to predict vectorized lane representations.<n>Experiments are conducted on an internal dataset as well as on the public datasets nuScenes and nuPlan.
arXiv Detail & Related papers (2026-04-27T11:47:16Z) - Unifying Environment Perception and Route Choice Modeling for Trajectory Representation Learning [47.00223863430964]
Tray Learning (TRL) aims to encode raw trajectories into low-dimensional vectors, which can be leveraged in various downstream tasks, including travel time estimation, location prediction, and trajectory similarity analysis.<n>We propose a framework that unifies comprehensive environment textbfPertemporal explicit textRoute choice modeling for effective textbfPRTrajectory representation learning, dubbed textbfPRTraj.
arXiv Detail & Related papers (2025-10-16T15:55:28Z) - Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving [2.905122328210335]
Lane-topology prediction is a critical component of safe and reliable autonomous navigation.<n>We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality.<n>We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model.
arXiv Detail & Related papers (2025-06-12T03:02:01Z) - Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition [11.293184441610283]
Current online map matching methods are prone to errors in complex road networks.<n>We propose an online Standard Definition (SD) map matching method by constructing a Hidden Markov Model (HMM) with multiple probability factors.<n>Our proposed method can achieve accurate map matching even in complex road networks by carefully leveraging lane markings and scenario recognition.
arXiv Detail & Related papers (2025-05-08T07:24:52Z) - InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method [10.561470037080177]
We present a novel LiDAR-based method for online vehicle-centric intersection localization.<n>We detect intersection candidates in a bird's eye view (BEV) representation formed by concatenating semantic road scans.<n>Experiments on the Semantic KITTITI dataset show that our method outperforms the latest learning-based baseline in accuracy and reliability.
arXiv Detail & Related papers (2025-05-01T13:30:28Z) - 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) - NLP-enabled Trajectory Map-matching in Urban Road Networks using a Transformer-based Encoder-decoder [1.3812010983144802]
This study introduces a data-driven, deep learning-based map-matching framework, formulating the task as machine translation, inspired by NLP.<n>A transformer-based encoder-decoder model learns contextual representations of noisy GPS points to infer trajectory behavior and road structures in an end-to-end manner.<n>Experiments on synthetic trajectories show that this approach outperforms conventional methods by integrating contextual awareness.
arXiv Detail & Related papers (2024-04-18T18:39:23Z) - Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
arXiv Detail & Related papers (2023-12-13T23:06:30Z) - OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure.
OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes.
We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
arXiv Detail & Related papers (2023-04-20T16:31:22Z) - Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view.
Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
arXiv Detail & Related papers (2022-11-15T13:52:41Z) - Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem.
We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks.
Our method can generate high-quality fine-grained traffic flow maps.
arXiv Detail & Related papers (2021-09-29T07:51:49Z)
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.