A Novel Multi-layer Task-centric and Data Quality Framework for Autonomous Driving

TL;DR

This paper introduces a multi-layer framework for assessing and managing data quality in autonomous vehicles, emphasizing task-centric approaches to improve system robustness and performance in dynamic environments.

Contribution

It proposes a novel five-layer data quality framework tailored for autonomous driving, integrating data, task, and goal layers to enhance decision-making and system resilience.

Findings

Redundancy removal can improve object detection performance.

Multimodal data analysis reveals data quality issues.

Framework guides adaptive and explainable AV development.

Abstract

The next-generation autonomous vehicles (AVs), embedded with frequent real-time decision-making, will rely heavily on a large volume of multisource and multimodal data. In real-world settings, the data quality (DQ) of different sources and modalities usually varies due to unexpected environmental factors or sensor issues. However, both researchers and practitioners in the AV field overwhelmingly concentrate on models/algorithms while undervaluing the DQ. To fulfill the needs of the next-generation AVs with guarantees of functionality, efficiency, and trustworthiness, this paper proposes a novel task-centric and data quality vase framework which consists of five layers: data layer, DQ layer, task layer, application layer, and goal layer. The proposed framework aims to map DQ with task requirements and performance goals. To illustrate, a case study investigating redundancy on the nuScenes dataset proves that partially removing redundancy on multisource image data could improve YOLOv8 object detection task performance. Analysis on multimodal data of image and LiDAR further presents existing redundancy DQ issues. This paper opens up a range of critical but unexplored challenges at the intersection of DQ, task orchestration, and performance-oriented system development in AVs. It is expected to guide the AV community toward building more adaptive, explainable, and resilient AVs that respond intelligently to dynamic environments and heterogeneous data streams. Code, data, and implementation details are publicly available at: https://anonymous.4open.science/r/dq4av-framework/README.md.