轻量化时序分解提升联邦自动驾驶的实时性能
Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving
- 将大注意力图分解为小矩阵，降低模型复杂度
- 在三个数据集上超越最新方法，实现实时推理
- 适合资源受限的联邦学习场景，可部署于真实机器人
基于视觉的自动驾驶系统在仅依赖单帧图像时，难以应对复杂环境。引入历史图像帧或转向序列等时序数据可显著提升系统鲁棒性与适应性。然而，现有高性能方法通常依赖高资源消耗的融合网络，不适用于联邦学习。为此，本文提出轻量化时序Transformer分解方法，通过将大型注意力图分解为小型矩阵，实现对图像序列和转向数据的高效处理。该方法有效降低模型复杂度，支持快速权重更新与实时预测，同时利用时序信息提升驾驶性能。在三个数据集上的大量实验表明，该方法明显优于近期方法，并具备实时能力。真实机器人实验进一步验证了其有效性。
原文摘要 · Abstract (English)
Traditional vision-based autonomous driving systems often face difficulties in navigating complex environments when relying solely on single-image inputs. To overcome this limitation, incorporating temporal data such as past image frames or steering sequences, has proven effective in enhancing robustness and adaptability in challenging scenarios. While previous high-performance methods exist, they often rely on resource-intensive fusion networks, making them impractical for training and unsuitable for federated learning. To address these challenges, we propose lightweight temporal transformer decomposition, a method that processes sequential image frames and temporal steering data by breaking down large attention maps into smaller matrices. This approach reduces model complexity, enabling efficient weight updates for convergence and real-time predictions while leveraging temporal information to enhance autonomous driving performance. Intensive experiments on three datasets demonstrate that our method outperforms recent approaches by a clear margin while achieving real-time performance. Additionally, real robot experiments further confirm the effectiveness of our method.
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