用跳层Swin Transformer实现端到端自动驾驶路径点预测
SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer
- 引入跳层Swin结构，增强多层级特征表达能力
- 在CARLA对抗场景中驾驶得分优于现有方法
- 适合研究自动驾驶感知与规划融合的开发者
针对具备像素级上下文感知能力的端到端自动驾驶模型研发需求，本文提出SKGE-Swin架构。该架构采用带跳层机制的Swin Transformer，通过基于移位窗口的多头自注意力（SW-MSA）机制，实现对远距离像素信息的提取，并在特征提取全过程保留关键信息，提升对复杂环境模式的理解能力。模型在CARLA平台的对抗性场景下进行评估，实验结果表明，SKGE-Swin架构在驾驶得分上优于先前方法。此外，还将开展消融实验，验证跳层连接与Swin Transformer使用对性能提升的贡献。
原文摘要 · Abstract (English)
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.
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