研究端到端自动驾驶的数据量与性能关系，发现数据越多效果越好。
Data Scaling Laws for End-to-End Autonomous Driving
- 用简单端到端模型测试不同数据量下的驾驶表现
- 在16到8192小时数据上验证，性能随数据增长而提升
- 为自动驾驶数据收集提供量化决策依据
自动驾驶系统传统上采用模块化设计，分别处理感知、预测和规划，但这种结构在模块间通信时会损失信息，增加计算开销，并可能导致误差累积。为此，近期研究提出将所有组件整合为一个端到端可微分模型，实现整体优化。这一转变更强调数据工程而非软件集成，仅通过扩大训练资源即可提升系统性能。本文在内部驾驶数据集（规模从16至8192小时）上评估了一个简单端到端驾驶架构的性能，使用开环指标与闭环仿真进行分析。具体研究了达到目标性能提升（如运动预测准确率提高5%）所需的数据增量。通过揭示模型性能与训练数据量之间的关系，旨在为自动驾驶开发中的数据驱动决策提供参考。
原文摘要 · Abstract (English)
Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introduces information loss during inter-module communication, increases computational overhead, and can lead to compounding errors. To address these challenges, recent works have proposed architectures that integrate all components into an end-to-end differentiable model, enabling holistic system optimization. This shift emphasizes data engineering over software integration, offering the potential to enhance system performance by simply scaling up training resources. In this work, we evaluate the performance of a simple end-to-end driving architecture on internal driving datasets ranging in size from 16 to 8192 hours with both open-loop metrics and closed-loop simulations. Specifically, we investigate how much additional training data is needed to achieve a target performance gain, e.g., a 5% improvement in motion prediction accuracy. By understanding the relationship between model performance and training dataset size, we aim to provide insights for data-driven decision-making in autonomous driving development.
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