Autonomous Vehicles

Powering Autonomous Vehicles with High-quality Training Data

Highly accurate AI training data for autonomous vehicles that are error-free, human-labeled, and cost-effective

Automotive Ai

Featured Clients

Empowering teams to build world-leading AI products.

Amazon
Google
Microsoft
Cogknit

There’s an increasing demand for automotive datasets to train Machine Learning models, & AI plays a critical role by processing massive volumes of data that are far beyond our control.

Cars & automobiles in general play a crucial role in our daily lives and most people would not deny the fact that driverless cars are the future that is set to revolutionize how we commute.

According to Goldman Sachs, the next 10 years are crucial for the auto industry as it will undergo a major transformation: the cars themselves,  the companies that build them, and the customers – all will look significantly different than what it was before.

Industry:

With $4.5 billion dollars in investment in 2019 AVs have the potential to revolutionize the automobile industry, improve safety, reduce congestion, energy consumption, & pollution.

Industry:

As per a recent report by IHS Markit, it is forecasted that roughly 33 million AVs will hit the road by 2040 contributing to  26 percent of new car sales.

According to a recent report by Allied Market Research, the global autonomous vehicle market is projected to reach $556.67 billion by 2026, registering a CAGR of 39.47% from 2019 to 2026.

A healthy amount of automotive expertise

Empowering emerging technologies to ride the next wave of Connected Vehicles. Shaip is a leading AI Data Platform, providing high-quality data collection and annotation that powers ML & AI applications across the automotive industry.

Data Collection Services

Automotive Image Data Collection

Image Data Collection for Automotive

We offer large volumes of image datasets (person, vehicle, traffic signs, road lanes) to train autonomous vehicles in a variety of scenarios and situations. Our experts can collect relevant image datasets as per your project requirements.

Automotive Video Data Collection

Video Data Collection for Automotive

Collect actionable training video datasets like vehicular movement, traffic signals, pedestrians, etc. to train autonomous vehicles ML models. Each dataset is tailored specifically to meet your specific use case.

Data Annotation Services

We have one of the most advanced image/video annotation tools in the
market that makes image labeling precise and super-functional for
complex use cases such as autonomous driving where quality is of utmost importance. Images & Videos are categorized frame by frame into objects such as pedestrians, vehicles, roads, lamp posts, traffic signs, etc. to build high-quality training data.

Automotive Data Annotation Services

Data Annotation Techniques for Self Driving Cars

We help you with diverse labeling techniques after carefully studying your automotive project scope. We have a dedicated workforce trained for such complex annotation, QA teams that ensure 95%+ tagging accuracy levels, and tools to automate quality checks. Depending on your machine learning project, we would work on one or a combination of these image annotation techniques:

Lidar

LIDAR

We can label images or videos with 360-degree visibility, captured by high-resolution cameras, to build high-quality, ground truth datasets that power autonomous vehicles algorithm.

Bounding Boxes

BOUNDING BOXES

Our experts use the box annotation technique to map objects in a given image/video to build datasets thereby enabling ML models to identify & localize objects.

Polygon Annotation

POLYGON ANNOTATION

In this technique, annotators plot points on object's (like Edge of Road, Broken Lane, End of Lane) exact edges to be annotated, regardless of their shape

Semantic Segmentation

SEMANTIC SEGMENTATION

In this technique, every pixel in an image/video is annotated with information & separated into different segments you need your cv algorithm to recognize

Object Tracking

OBJECT TRACKING

Auto-detect instances of semantic objects of a certain class in digital images and videos, use cases could include face detection and pedestrian detection.

Use Cases

Driver Monitoring

Driver Monitoring System

Build highly accurate driver monitoring system by annotating facial landmarks such as eyes, head, mouth, etc. with accuracy & relevant metadata for blink detection and gaze estimation.

Pedestrian Tracking

Pedestrian Tracking System

Annotate pedestrians in various images with 2D bounding boxes, to build high-quality training data for pedestrian tracking

Automated Driver Assistance

Automated Driver Assistance System

Semantic Segmentation of images/videos frame by frame which includes objects such as pedestrians, vehicles – (cars, bicycles, buses), roads, lamp posts for building high-quality training data for AI-based autonomous vehicle systems.

Object Detection

Object Detection

Annotate hrs of images/videos frames of urban and street environments including cars, pedestrians, lamp posts, etc. to facilitate object detection to build high-quality training data for developing CV models for autonomous vehicle.

Driver Drowsiness / Fatigue Detection

Reduce road accidents caused by drivers falling asleep by gathering vital driver information from facial landmarks such as drowsiness, eye gaze, distraction, emotion, & more. These in-cabin images are accurately annotated and used for training ML models.

Car Voice Assistant

In-cabin Voice Assistant

Enhance Voice recognition in car or car's voice assistant by enabling drivers to make phone calls, control music, place orders, book services, schedule appointments & more. We offer vernacular datasets in 50+ languages to train your Car Voice Assistant.

Why Shaip?

Managed workforce for complete control, reliability & productivity

A powerful platform that supports different types of annotations

Minimum 95% accuracy ensured for superior quality

Global projects across 60+ countries

Enterprise-grade SLAs

Best-in-class real-life driving data sets

Autonomous Driving Datasets

Car Interior Image Dataset

Annotated images (along with metadata) of different car interiors from multiple brands

Car Interior Image Dataset With Segmentation

  • Use Case: Car Interior Image Recognition
  • Format: Images
  • Annotation: Segmentation

Outdoor Image Dataset

Images of outdoor environments of street-level in urban areas or on highways with frequent traffic

Outdoor Image Dataset With Annotation

  • Use Case: Image Anonymization Solution
  • Format: Images
  • Annotation: Yes

Car Driver in focus Image Dataset

Images of driver’s face with car setup in different poses and variations covering unique participants from multiple ethnicities

Car Driver In Focus Image Dataset

  • Use Case: In-car ADAS model
  • Format: Images
  • Annotation: No

Vehicle License Plate Dataset

Images of Vehicle License Plates from different angles

Vehicle License Plate Dataset

  • Use Case: Object Detection
  • Format: Images
  • Annotation: No

Our Capability

People

People

Dedicated and trained teams:

  • 30,000+ collaborators for Data Creation, Labeling & QA
  • Credentialed Project Management Team
  • Experienced Product Development Team
  • Talent Pool Sourcing & Onboarding Team

Process

Process

Highest process efficiency is assured with:

  • Robust 6 Sigma Stage-Gate Process
  • A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
  • Continuous Improvement & Feedback Loop

Platform

Platform

The patented platform offers benefits:

  • Web-based end-to-end platform
  • Impeccable Quality
  • Faster TAT
  • Seamless Delivery

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