BEV-Guided Multi-Modality Fusion for Driving Perception
Author:
Affiliation:
1. UIUC
Funder
NSF
NIFA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203805.pdf?arnumber=10203805
Reference49 articles.
1. Radar/Lidar sensor fusion for car-following on highways
2. ImageNet: A large-scale hierarchical image database
3. Deep Residual Learning for Image Recognition
4. Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
5. A multi-sensor fusion system for moving object detection and tracking in urban driving environments
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1. Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving;Engineering Applications of Artificial Intelligence;2024-09
2. BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02
3. BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird’s Eye View Map Construction;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
4. Predicting Bird's-Eye-View Semantic Representations Using Correlated Context Learning;IEEE Robotics and Automation Letters;2024-05
5. DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01
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