CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
This addresses data efficiency for autonomous driving systems, but it is incremental as it builds on existing imitation learning and VQ-VAE techniques.
The paper tackles the problem of imbalanced training datasets in imitation learning for autonomous driving by introducing CAPS, which uses VQ-VAEs to cluster data and prioritize rare samples, resulting in improved generalization and outperforming state-of-the-art methods in CARLA simulations.
In this paper, we introduce CAPS (Context-Aware Priority Sampling), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced training datasets in imitation learning by leveraging Vector Quantized Variational Autoencoders (VQ-VAEs). The use of VQ-VAE provides a structured and interpretable data representation, which helps 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. By ensuring a more diverse and informative training set, CAPS improves the generalization of the trained planner across a wide range of driving scenarios. We evaluate our method through closed-loop simulations in the CARLA environment. The results on Bench2Drive scenarios demonstrate that our framework outperforms state-of-the-art methods, leading to notable improvements in model performance.