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.

翻译：本文提出了一种新颖的上下文感知优先级采样方法，旨在提升基于学习的自动驾驶系统的数据利用效率。该方法通过利用矢量量化变分自编码器，解决了模仿学习中数据集不平衡的挑战。由此可获得结构化且可解释的数据表示，有助于揭示数据中的有效模式。这些模式被用于将数据分组为若干簇，每个样本被分配一个簇标识符。随后利用簇标识符对数据集进行重平衡，确保在训练过程中稀有但高价值的样本获得更高优先级。我们在CARLA仿真器中通过闭环实验对所提方法进行了评估。Bench2Drive场景下的实验结果表明，该方法能有效提升模型泛化能力，在驾驶评分与成功率方面均取得显著改善。