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.
By Run Wang, Chaoyi Zhou, Amir Salarpour, Xi Liu, Zhi-Qi Cheng, Feng Luo, Mert D. Pes\'e, Siyu Huang
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.
By Felipe Carlos dos Santos, Eric Antonelo, Gustavo Claudio Karl Couto
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.
By Richard Schwarzkopf, Fabian Immel, Alexander Blumberg, Jonas Merkert, Nils Rack, Kaiwen Wang, Fabian Konstantinidis, Julian Truetsch, Carlos Fernandez, Annika B\"atz, Kevin R\"osch, Marlon Steiner, Willi Poh, Yinzhe Shen, Royden Wagner, Felix Hauser, Dominik Strutz, Jaime Villa, Gleb Stepanov, Holger Caesar, \"Omer \c{S}ahin Ta\c{s}, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller
arXiv:2606. 17080v1 Announce Type: cross Abstract: Reliable autonomous driving requires vectorized HD maps that are geometrically accurate, semantically rich, and scalable to long-horizon driving.
By Sahith Reddy Chada, Isht Dwivedi, Nirav Savaliya
arXiv:2607. 04541v1 Announce Type: cross Abstract: Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning.
By Jingyu Song, Yi Liu, Katherine A. Skinner
arXiv:2602. 00222v3 Announce Type: replace-cross Abstract: Vision-Language Navigation (VLN) requires agents to follow natural language instructions in partially observed 3D environments, motivating map representations that aggregate spatial context beyond local perception.
By Guoxin Lian, Shuo Wang, Yucheng Wang, Yongcai Wang, Maiyue Chen, Kaihui Wang, Bo Zhang, Zhizhong Su, Deying Li, Zhaoxin Fan
arXiv:2606. 15930v1 Announce Type: cross Abstract: Simulation is central to validating autonomous driving systems, yet current pipelines are limited by insufficient scenario diversity due to costly High Definition (HD) map creation.
By Marwan Farag, Steffen W\"aldele, Yu Yao
arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.
By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis
Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in traditional modular pipelines. However, current state-of-the-art approaches rely predominantly on geometric supervision, such as occupancy regression and optical flow, effectively treating scene agents as generic moving obstacles.
arXiv:2511.22187v4 Announce Type: replace
Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support no...
By Qiang Li, Yingwenqi Jiang, Tuoxi Li, Duyu Chen, Xiang Feng, Yucheng Ao, Shangyue Liu, Xingchen Yu, Youcheng Cai, Yumeng Liu, Yuexin Ma, Xin Hu, Li Liu, Yu Zhang, Linkun Xu, Bingtao Gao, Xueyuan Wang, Shuchang Zhou, Xianming Liu, Ligang Liu
arXiv:2606. 04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive.
By Deguo Xia, Zihan Li, Haochen Zhao, Dong Xie, Yuyao Kong, Xiyan Liu, Jizhou Huang, Mengmeng Yang, Diange Yang
arXiv:2609.06195v1 Announce Type: new
Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds...
By Azusa Sawada, Allan Wang, Hideo Saito, Aaron Steinfeld