The Automotive Issue: Auto-labeling, pre-annotations, and HITL within automotive

By Duncan Curtis , SVP AI Product and Technology

AI-driven auto-labeling excels in handling large volumes of straightforward annotations, and doing so allows human annotators to focus on more important challenges in the data. However, when it comes to edge cases and complex scenarios—especially in long sequences—human judgment is irreplaceable.

Adding a human-in-the-loop (HITL) ensures that the critical elements of complex data, such as ambiguous objects or rare occurrences, are reviewed and corrected by human experts, delivering the high level of accuracy that’s critical for ADAS models.?

A hybrid approach optimizes both speed and precision. Here are three other benefits:

Efficiency gains from leveraging pre-annotation models.?

• Pre-annotation models can dramatically reduce human annotation time by generating initial labels which human experts quickly review and refine.?

Improved model performance by focusing on edge cases.

• Instead of annotating full datasets from scratch, more project time and resources can be spent on these rare, nuanced, and potentially critical instances.

Shorter time to delivery for long sequences.?

• Shifting workflows and processes toward long sequences vs. single frames—like we do at Sama—improves annotation accuracy by capturing contextual relationships and temporal dynamics. The combination of ML-powered extrapolation technologies and enhanced human workflow capabilities enable a distributed labeling process, which means we deliver the work sooner without seeing a drop in quality.

These benefits are further examined in our latest free guide, Accelerating Automotive Data Labeling.

Watch Now: Insights-driven solutions to annotating long sequences in ADAS

As we gain more efficiency from ever-better ML models, we are left with increasingly complex ML use cases that, in turn, pose greater HITL challenges. In this presentation originally from Tech AD USA, Ryan Tavakolfar shared examples of addressing the complex HITL challenges of annotating long sequences and using pre-annotations, citing insights-driven approaches and solutions.

Sama launched a scalable medium-length sequence annotation solution for Automotive AI—with more to come

Our customers work with us because they need a partner that can handle the complexity inherent to annotating automotive data, including medium and long sequences.

So we created a scalable solution specifically designed for high-quality, rapid annotation of medium sequences of frames, which are essential for automotive AI models.

To date, Sama has successfully used its proprietary method with medium-length sequences of up to hundreds of miles of contiguous driving and is working to scale to even longer sequences of frames in a further expansion of the company’s capabilities.

Learn more here

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