Data labeling for autonomous vehicle training

Discover how SUPA's specialized data labeling services enhanced an autonomous driving company's models, achieving 95% accuracy

Problem statement

The client needed to enhance their autonomous driving model's accuracy in interpreting vector spaces to meet safety standards and effectively navigate diverse environments.

SOLUTION

SUPA provided expert data labeling services, combining rigorous annotator training and human-machine collaboration, along with a dedicated quality control team to ensure high-quality, consistent data.

RESULT

SUPA continues to deliver labeled data to the Client with 95% accuracy, significantly improving the client’s model predictions and meeting tight delivery timelines since 2022.

Overview

The Problem

Our client, a leading autonomous driving company, faced challenges in improving their model’s accuracy in capturing vector space. Their vision-based model needed to meet stringent safety requirements and accurately interpret the surrounding environment to ensure the safe navigation of autonomous vehicles.

The Solution

SUPA provided a specialized data labeling service tailored to address these specific challenges. Our approach included:

Expert Training and Assessment: We conducted rigorous training programs for our annotators, equipping them with the skills needed to accurately label complex elements within diverse driving scenes.
Human-Machine Collaboration: By combining human expertise with advanced machine learning tools, we ensured the production of high-quality labels. This approach was critical for handling the wide variety of international driving environments and objects encountered by autonomous vehicles.
Elite Quality Control Team: To maintain the highest standards of reliability and consistency, we trained a dedicated quality control team. This team worked closely with the client to verify the accuracy of the labeled data, ensuring it met the required standards.

The Result

SUPA continues to successfully meet the client's tight delivery timelines, achieving a 95% accuracy rate in the labeled data since 2022. This high level of precision continues to significantly improve the client's model predictions, contributing to the enhanced safety and performance of their autonomous driving systems in planned releases.

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STEM Dataset

Bilingual Multimodal STEM Dataset — a curated collection of 500 Math and Physics questions in Malay and English, some enriched with relevant images.

Location:

AI models often struggle with bilingual and multimodal STEM tasks due to a lack of high-quality, domain-specific datasets in languages like Malay and English.

Type of Work:

Large Language Models

Completion Date:

500 high-quality Math and Physics questions

Evaluation Leaderboard

STEM-focused AI evaluation

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