Bird's-Eye-View Segmentation

Latest papers

11 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
Sep 5, 2026Felipe Carlos dos Santos, Eric Antonelo, Gustavo Claudio Karl CoutoBird's-Eye-View Segmentation Bird's-Eye-View Representation
Federal University of Santa Catarina (UFSC), Florianópolis, Brazil Department of Automation and Systems Engineering
CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation
Aug 14, 2026Jinlong Wang, Yuang Jia, Junhong Lin +2Bird's-Eye-View Segmentation Cooperative Perception
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School Peking University Shenzhen, China · School of Computer Science and Engineering Macau University of Science and Technology Macau, China
Variational Inference for Bird's Eye View Segmentation in Autonomous Driving
Jul 16, 2026Jingyue Shi, Huaicheng Li, Junhui Zhao +1Multi-View Learning Bird's-Eye-View Representation
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China · School of Information Science and Engineering, Southeast University, Nanjing 210096, China
Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints
Jun 23, 2026Hojun Choi, Seulbin Hwang, Dae Jung Kim +3VLMs for Autonomous Driving Open-Vocabulary Semantic Segmentation
KAIST AI, South Korea · NAVER LABS, South Korea
BEV-Denoise: Learning Intrinsic Noise for Accurate Bird's-Eye-View Semantic Segmentation
Jun 22, 2026Dooseop Choi, Kyounghwan An, Kyoung-Wook MinBird's-Eye-View Representation Bird's-Eye-View Segmentation
Electronics and Telecommunications Research Institute (ETRI) · University of Science and Technology (UST)
Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation
May 20, 2026Abhishek Dinkar Jagtap, Sanath Tiptur Sadashivaiah, Andreas FestagHypernetworks V2X Communication
CARISSMA Institute for Electric, COnnected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Ingolstadt, 85049, Germany. · University of Applied Sciences Aschaffenburg, Aschaffenburg, 63743, Germany.
4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
May 18, 2026Kane Qian, Xin Zhao, Yining Shi +7Autonomous Driving Datasets Autonomous Driving
1Tsinghua University. · 2Hesai Technology Co., Ltd.
GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
Apr 21, 2026Joshua Niemeijer, Alaa Eddine Ben Zekri, Reza Bahmanyar +3Autonomous Driving Perception Bird's-Eye-View Representation
German Aerospace Center (DLR) · Digital Research Center of Sfax
CycleBEV: Regularizing View Transformation Networks via View Cycle Consistency for Bird's-Eye-View Semantic Segmentation
Feb 27, 2026Jeongbin Hong, Dooseop Choi, Taeg-Hyun An +2Bird's-Eye-View Segmentation Cycle Consistency
Electronics and Telecommunications Research Institute (ETRI) · University of Science and Technology (UST)
Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Feb 20, 2026Daniel Busch, Christian Bohn, Thomas Kurbiel +3Bird's-Eye-View Segmentation Autonomous Driving Perception
University of Wuppertal · APTIV
GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
Feb 9, 2026Santiago Montiel-Marín, Miguel Antunes-García, Fabio Sánchez-García +3Camera-Radar Fusion Autonomous Driving Perception
Department of Electronics. University of Alcalá, Spain. · Department of Cognitive Robotics. Delft University of Technology, The Netherlands.

Sep 5, 2026J/K move · Enter open · S save

Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies

Felipe Carlos dos Santos, Eric Antonelo, Gustavo Claudio Karl Couto

Federal University of Santa Catarina (UFSC), Florianópolis, Brazil Department of Automation and Systems Engineering

Bird's-Eye-View Segmentation Bird's-Eye-View Representation Autonomous Driving Perception End-to-End Autonomous Driving Behavior Cloning Transformer

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In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become a key input modality for Behavioral Cloning (BC) policies. While ground-truth BEV maps are readily available in simulation, real-world deployment requires replacing them with camera-predicted counterparts - a substitution that introduces perceptual errors whose downstream impact on closed-loop driving performance is not well understood. In this work, we investigate the use of Cross-View Transformer (CVT)-predicted BEV maps as direct policy inputs for a BC agent in the CARLA simulator. We propose a six-channel BEV representation covering road surface, planned route, lane boundaries, vehicles, pedestrians, and traffic lights, and introduce a Kernel Density Estimation (KDE) weighting scheme that rebalances the segmentation loss towards underrepresented driving maneuvers such as curves and intersections. Closed-loop evaluation across two CARLA towns shows that the KDE-weighted model is the only predicted-BEV agent to complete a full episode without infractions, despite not achieving the highest aggregate IoU. This discrepancy reveals that global segmentation metrics are poor proxies for driving performance: what determines navigation success is prediction quality at geometrically critical locations, and the route channel emerges as the primary bottleneck for reliable agent navigation under predicted BEV inputs.