用场景嵌入选关键数据，30%样本达全量效果
Active Learning from Scene Embeddings for End-to-End Autonomous Driving
- 基于鸟瞰图特征自动筛选高价值驾驶场景
- 仅用nuScenes数据的30%即逼近全量训练性能
- 适合数据标注成本高的自动驾驶研发团队
在自动驾驶领域，端到端深度学习模型能直接从传感器数据中学习驾驶决策，但训练需大量标注数据，成本高昂。由于真实驾驶数据呈现长尾分布，简单场景占多数，我们提出识别最具挑战性的场景以提升模型效率。现有方法依赖人工设计策略，泛化性差。观察到端到端模型中的鸟瞰图（BEV）特征包含完整场景信息，我们提出SEAD主动学习框架：初始数据基于驾驶环境信息选择，增量数据则基于BEV特征动态筛选。实验表明，仅使用nuScenes训练数据的30%，即可达到接近全量数据的性能表现。代码将公开。
原文摘要 · Abstract (English)
In the field of autonomous driving, end-to-end deep learning models show great potential by learning driving decisions directly from sensor data. However, training these models requires large amounts of labeled data, which is time-consuming and expensive. Considering that the real-world driving data exhibits a long-tailed distribution where simple scenarios constitute a majority part of the data, we are thus inspired to identify the most challenging scenarios within it. Subsequently, we can efficiently improve the performance of the model by training with the selected data of the highest value. Prior research has focused on the selection of valuable data by empirically designed strategies. However, manually designed methods suffer from being less generalizable to new data distributions. Observing that the BEV (Bird's Eye View) features in end-to-end models contain all the information required to represent the scenario, we propose an active learning framework that relies on these vectorized scene-level features, called SEAD. The framework selects initial data based on driving-environmental information and incremental data based on BEV features. Experiments show that we only need 30\% of the nuScenes training data to achieve performance close to what can be achieved with the full dataset. The source code will be released.
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