Unlock the value of your raw image and video data with full data transparency.
Find everything and anything - and much more.
Data intelligence for autonomous driving
The Challenge
Manual data exploration and guesswork are THE bottleneck for labelling and annotation of large datasets.
Put simply: nobody knows what's in these 500 million frames collected.
This lack of transparency leads to guesswork, wasted effort, time and money resulting in labelling blindly, spending over budget and a shotgun approach to data preparation.
Our Solution
Explore raw frames and videos with natural language and the power of semantic search.
Understand every detail of the captured raw data for AD&ADAS development; from objects to events from road conditions to number of objects.
Quasara Plato provides a bird's eye view on raw datasets with natural language and great solutions dedicated for AD datasets.
Accuracy Boost on Workload Planning
Blind quotations based on guesswork is the norm.
Quasara Plato helps to achieve complete data transparency for understanding the distribution and complexity of datasets.
Reduce project risk - quote based on accurate workloads and improve ROI.
Reduce project risk by up to 90%.
Automation of Tagging Workflows
Image/video labelling, tagging and annotating is a highly manual, costly and time-consuming task and teams are often forced to start at 0.
We help to auto-tag images based on search results like “car”, “truck”, “stop sign” in minutes.
Reduce data labelling costs by up to 50%.
Focused Labelling on High Value Objects & Driving Scenarios
There is an almost endless number of edge cases and bizarre driving events that need to be managed by autonomous vehicles.
Laser-focus on the most value-adding datapoints, aka edge cases or outliers.
Only train models with data that AD systems have not been trained on, and ignore the rest.
Use Cases
Plato provides transparency & data intelligence for ADAS & AD Datasets
Scenario Data Catalogue Enrichment
Like "Intersection with traffic lights"
Edge Case Detection
Like "Animals running across the street"
Complexity Assessment
"Countryside" vs. "Downtown" vs. "Highway" driving
Our Solution
Unlocking Enterprise Data With Semantic Search
Edge Case Detection
Find out all anomalous objects and situations that are outliers
Scenario Data Catalogue Enrichment
Focus on certain scenarios that need more attention
Data Complexity Assessment
Don't limit your datasets- include every real-world situation
Find complex relational driving situation or edge cases in your unlabelled datasets at a semantic level, right out-of-the-box, in a fraction of the usual time.
Find dangerous driving situations and absolute outliers in your data, ver your datasets to improve your models for your next computer vision project.
