Autonomous Vehicles & Transportation

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are critical in orchestrating the development and enhancement of Autonomous Vehicles & Transportation AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the data that powers these safety-critical systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand transportation dynamics, sensor data, and annotation goals. For example, in LiDAR annotation, PMs might train workers to distinguish overlapping objects, using sample point clouds and guides. Onboarding includes hands-on tasks like tagging traffic signs, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as multi-tier reviews for high-stakes accident data.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 20,000 LiDAR frames) and set metrics like precision, consistency, or detection rates. They monitor progress via dashboards, address inefficiencies, and refine guidelines based on worker feedback or evolving transportation standards.

Collaboration with AI Teams

PMs connect data curators with machine learning engineers, translating technical requirements (e.g., real-time object detection) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI deployment cycles.

We Manage the Tasks Performed by Workers

The annotators, taggers, or analysts perform the detailed work of preparing high-quality datasets for autonomous vehicles and transportation. Their efforts are meticulous and spatially aware, requiring precision and context.

Common tasks include:

Labeling and Tagging

For road recognition, we might tag an image as “yield sign.” In accident detection, they label sensor data with “near miss” or “safe.”

Contextual Analysis

For driver behavior, our team assesses footage, tagging “looking away” or “hands on wheel.” In aerial annotation, they analyze drone images, tagging “traffic jam.”

Flagging Violations

In LiDAR annotation, our employees and subcontractors flag unclear data (e.g., obscured points), ensuring reliability. In risk prediction, they mark ambiguous events.

Edge Case Resolution

We handle complex cases—like low-visibility signs or rare driving scenarios—often requiring discussion or escalation to transportation experts.

We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary AV tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated data required to train and refine Autonomous Vehicles & Transportation AI systems is immense, driven by the complexity of real-world driving conditions. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 10,000–50,000 labeled samples per category (e.g., 50,000 tagged traffic signs). For tasks like LiDAR annotation, this could rise to 100,000 to cover 3D scenarios.

Iterative Refinement

To improve accuracy (e.g., from 85% to 95%), an additional 5,000–20,000 samples per issue (e.g., missed objects) are often needed. For example, refining accident detection might demand 10,000 new frames.

Scale for Robustness

Large-scale systems (e.g., fleet-wide autonomy) require datasets in the millions to handle edge cases, weather, or rare events. A road recognition model might start with 200,000 frames, expanding by 50,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain data for review. This reduces volume but requires ongoing curation—perhaps 1,000–5,000 samples weekly—to maintain performance.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and safety.

Multilingual & Multicultural Autonomous Vehicles & Transportation AI

We can assist you with your autonomous vehicles and transportation AI needs across diverse linguistic and cultural contexts.

Our team is equipped to curate and process data for global transportation systems, ensuring accurate and regionally relevant datasets tailored to your objectives.

We work in the following languages: