Resolve multimodal spatiotemporal annotation alignment

Resolve the spatiotemporal alignment problems in multimodal autonomous-driving data annotation so that annotations are temporally synchronized and geometrically consistent for effective end-to-end autonomous-driving training.

Background

The survey identifies annotation as a major bottleneck in value-driven data governance for end-to-end autonomous driving. Multimodal datasets require accurate temporal synchronization and geometric consistency across sensors, yet current annotation pipelines remain inefficient and consume substantial resources, particularly when low-value samples receive expensive manual processing.

The unresolved alignment problem limits the reliability of supervision and reduces the effective density of annotated data. Addressing it is presented as necessary for scalable, cost-efficient training and for prioritizing detailed annotation on high-value scenarios.

References

Additionally, annotation processes are inefficient, especially in multimodal data processing where spatiotemporal alignment problems remain unsolved, and lack of hierarchical management leads to high-cost annotation consumption on low-value samples.

— A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform  (2610.00926 - Xu et al., 1 Oct 2026) in Section 6.1, “Scenario Prioritization and Data Engine: From ‘Data Volume’ to ‘Value Density’”