A Novel Multilayer Task-Centric and Data Quality Framework for Autonomous Driving

The next-generation autonomous vehicles (AVs), embedded with frequent real-time decision-making, will rely heavily on massive multisource and multimodal data. In real-world settings, the data quality (DQ) of different sources and modalities 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 improve AV’s functionality, efficiency, and trustworthiness, this paper proposes a novel task-centric and data quality vase framework, which consists of five layers: data, DQ, task, application, and goal. The proposed framework aims to map DQ with task requirements and performance goals. 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.