mlsanityData to 14 Oct 2026

SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer

arXiv 2508.20762 ↗First posted 28 Aug 2025cs.CV cs.AI cs.LGcited by 0

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Focusing on the development of an end-to-end autonomous vehicle model with pixel-to-pixel context awareness, this research proposes the SKGE-Swin architecture. This architecture utilizes the Swin Transformer with a skip-stage mechanism to broaden feature representation globally and at various network levels. This approach enables the model to extract information from distant pixels by leveraging the Swin Transformer's Shifted Window-based Multi-head Self-Attention (SW-MSA) mechanism and to retain critical information from the initial to the final stages of feature extraction, thereby enhancing its capability to comprehend complex patterns in the vehicle's surroundings. The model is evaluated on the CARLA platform using adversarial scenarios to simulate real-world conditions. Experimental results demonstrate that the SKGE-Swin architecture achieves a superior Driving Score compared to previous methods. Furthermore, an ablation study will be conducted to evaluate the contribution of each architectural component, including the influence of skip connections and the use of the Swin Transformer, in improving model performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 26 of its 37 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

26 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving31 May
  2. 2022End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent12 Apr
  3. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Driving19 Apr

Other references · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  7. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  8. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  9. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  10. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  11. 2017Mixed Precision Trainingcited by 1,156
  12. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
11 more
  1. 2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 675
  2. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  3. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
  4. 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 220
  5. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 212
  6. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 103
  7. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 93
  8. 2020End-to-end Contextual Perception and Prediction with Interaction Transformercited by 54
  9. 2020Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologiescited by 41
  10. 2019Looking to Relations for Future Trajectory Forecastcited by 23
  11. 2017Brain Inspired Cognitive Model with Attention for Self-Driving Carscited by 10

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving
  2. 2025InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer
  3. 2025PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
  4. 2024Enhancing End-to-End Autonomous Driving with Latent World Model
  5. 2025AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
  6. 2023KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks
  7. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  8. 2026End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems