arXiv AI
Generation of Vectorized Maps Beyond Vehicle View
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arXiv Computer Vision
Sep 4
FlexMap: Robust HD Map Construction under Flexible Camera Configurations
FlexMap is a vectorized high‑definition map construction framework that works with flexible camera configurations without needing calibrated rigs or explicit 2D‑to‑BEV transformations. It replaces geometric projection with a geometry foundation model that encodes cross‑view 3D structure, and uses a spatial‑temporal enhancement module and a camera‑aware decoder to separate spatial reasoning from temporal aggregation. Experiments on nuScenes and Argoverse 2 show that FlexMap outperforms pose‑dependent baselines and remains accurate even when camera views are missing or pose estimates are inaccurate.
arXiv Computer Vision
Sep 21
Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction
arXiv:2504.06647v4 Announce Type: replace Abstract: Safety-critical autonomous driving motivates the effective use of prior information. For online vectorized HD map construction, temporal prediction...
Hugging Face Trending Papers
4d ago
A Unified Benchmark for Aerial-Enhanced Autonomous Driving: The KITScenes Aerial Dataset
The paper introduces KITScenes Aerial, an extension of the KITScenes Multimodal dataset that adds synchronized aerial imagery and high‑resolution neural representations to existing onboard sensor data, vehicle poses, and Lanelet2 map annotations. It evaluates mapping across all 53 KITScenes map classes, showing that aerial context significantly improves fine‑grained and extended‑range mapping, with higher‑resolution imagery boosting performance further, especially for challenging elements like traffic lights. The study also assesses cross‑view localization under geographically disjoint conditions, achieving a mean translation error of 5.13 m with a fine‑grained feature‑matching baseline.
arXiv AI
Oct 5
From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models
arXiv:2610.02513v1 Announce Type: cross Abstract: Large-scale vectorized HD maps provide structured road information that is essential for perception, localization, and planning in autonomous driving...
arXiv AI
Sep 10
Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
The paper studies how Bird's‑Eye‑View (BEV) maps predicted by Cross‑View Transformers (CVT) can be used directly as inputs to a Behavior‑Cloning (BC) driving policy in the CARLA simulator. It introduces a six‑channel BEV representation and a Kernel Density Estimation (KDE) weighting scheme to focus learning on underrepresented maneuvers. Closed‑loop tests show that the KDE‑weighted model is the only predicted‑BEV agent to finish an episode without infractions, highlighting that global segmentation scores are poor proxies for driving performance and that prediction quality at critical geometries, especially the route channel, is key to reliable navigation.
arXiv Machine Learning
Jun 3
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.