通过轨迹熵最大化，40%数据量减少仍保持自动驾驶模型性能
Are All Data Necessary? Efficient Data Pruning for Large-scale Autonomous Driving Dataset via Trajectory Entropy Maximization
- 用轨迹分布熵衡量数据价值，迭代筛选高信息量样本
- 在NuPlan数据集上压缩40%数据，闭环性能几乎不变
- 理论可证减少分布偏移，适合大规模自动驾驶数据处理
收集大规模自然驾驶数据对训练鲁棒的自动驾驶规划器至关重要。然而，真实世界数据集常包含大量重复和低价值样本，导致存储成本过高且对策略学习收益有限。为此，我们提出一种基于信息论的数据剪枝方法，能在不损害模型性能的前提下显著减少训练数据量。该方法通过评估驾驶数据的轨迹分布信息熵，以模型无关的方式迭代选取能保留原始数据统计特性的高价值样本。理论上，最大化轨迹熵可有效约束剪枝子集与原始数据分布之间的Kullback-Leibler散度，从而保障泛化能力。在NuPlan基准和大规模模仿学习框架上的综合实验表明，该方法可将数据集规模减少高达40%，同时维持闭环性能。本工作为自动驾驶系统提供了轻量级、理论坚实的数据管理与高效策略学习方案。
原文摘要 · Abstract (English)
Collecting large-scale naturalistic driving data is essential for training robust autonomous driving planners. However, real-world datasets often contain a substantial amount of repetitive and low-value samples, which lead to excessive storage costs and bring limited benefits to policy learning. To address this issue, we propose an information-theoretic data pruning method that effectively reduces the training data volume without compromising model performance. Our approach evaluates the trajectory distribution information entropy of driving data and iteratively selects high-value samples that preserve the statistical characteristics of the original dataset in a model-agnostic manner. From a theoretical perspective, we show that maximizing trajectory entropy effectively constrains the Kullback-Leibler divergence between the pruned subset and the original data distribution, thereby maintaining generalization ability. Comprehensive experiments on the NuPlan benchmark with a large-scale imitation learning framework demonstrate that the proposed method can reduce the dataset size by up to 40% while maintaining closed-loop performance. This work provides a lightweight and theoretically grounded approach for scalable data management and efficient policy learning in autonomous driving systems.
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