用聚类优先采样提升自动驾驶模仿学习的数据效率
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
- 基于VQ-VAE提取数据结构化表示，按语义聚类并分配簇ID
- 对稀有但关键样本赋予更高训练优先级，改善数据分布
- 在CARLA仿真中显著提升驾驶得分与成功率，适合数据不平衡场景
本文提出上下文感知优先采样（CAPS），一种提升基于学习的自动驾驶系统数据效率的新方法。CAPS通过向量量化变分自编码器（VQ-VAE）解决模仿学习中数据分布不均的问题，获得结构化且可解释的数据表征，从而揭示数据中的有意义模式，并据此将样本聚类，为每个样本分配簇ID。这些簇ID用于重平衡数据集，确保罕见但重要的样本在训练中获得更高优先级。我们在CARLA模拟器中进行闭环实验，在Bench2Drive场景下的结果表明，CAPS能有效提升模型泛化能力，驾驶得分和成功率均有显著提高。
原文摘要 · Abstract (English)
In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced datasets in imitation learning by leveraging Vector Quantized Variational Autoencoders (VQ-VAEs). In this way, we can get structured and interpretable data representations, which help to reveal meaningful patterns in the data. These patterns are used to group the data into clusters, with each sample being assigned a cluster ID. The cluster IDs are then used to re-balance the dataset, ensuring that rare yet valuable samples receive higher priority during training. We evaluate our method through closed-loop experiments in the CARLA simulator. The results on Bench2Drive scenarios demonstrate the effectiveness of CAPS in enhancing model generalization, with substantial improvements in both driving score and success rate.
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