Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems

TL;DR

This paper introduces MOSAIC, a data selection framework that optimizes training data composition for autonomous driving models by leveraging neural scaling laws across data domains, leading to improved performance with less data.

Contribution

MOSAIC is a novel, scalable data selection method that accounts for domain-specific impacts on evaluation metrics, enhancing autonomous driving model training efficiency.

Findings

MOSAIC outperforms baseline data selection methods on EPDMS.

Achieves up to 80% reduction in training data needed.

Effectively models data domain impacts using neural scaling laws.

Abstract

Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, the training data, must address different evaluation criteria necessary for the models to be deployable in real-world environments. Data selection policies can guide the development of the training set, but current frameworks do not account for the ambiguity in how data points affect different metrics. In this work, we propose Mixture Optimization via Scaling-Aware Iterative Collection (MOSAIC), a general data selection framework that operates by: (i) partitioning the dataset into domains; (ii) fitting neural scaling laws from each data domain to the evaluation metrics; and (iii) optimizing a data mixture by iteratively adding data from domains that maximize the change in metrics. We apply MOSAIC to autonomous driving (AD), where an End-to-End (E2E) planner model is evaluated on the Extended Predictive Driver Model Score (EPDMS), an aggregate of driving rule compliance metrics. Here, MOSAIC outperforms a diverse set of baselines on EPDMS with up to 80\% less data.