An Initial Study of Bird's-Eye View Generation for Autonomous Vehicles using Cross-View Transformers
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Abstract (arXiv)
Bird's-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV's channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of different camera layouts, and two loss formulations (focal and L1). Using training data from only a town, a four-camera CVT trained with the L1 loss delivers the most robust test performance, evaluated in a new town. Overall, our results underscore CVT's promise for mapping camera inputs to reasonably accurate BEV maps.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 17 Aug 2025.
Builds on
13 of 21 references.
References · 13
- 2017Attention Is All You Needcited by 60,361
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
- 2017End-to-end Driving via Conditional Imitation Learningcited by 485
- 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
- 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
- 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
- 2022CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformerscited by 227
- 2019Cross-view Semantic Segmentation for Sensing Surroundingscited by 174
- 2023Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environmentscited by 12
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Citing papers per year: 2026: 2.
2 papers.