Every year, the Stanford Institute for Human-Centered Artificial Intelligence (HAI) puts out its AI Index, a massive collection of data and information that tries to sum up the current state of artificial intelligence.
Author Archives: Anyverse
AutoSens Detroit 2022 – Save the date! | Meet Anyverse
Save the date! This May 10-12, 2022, Anyverse, will be at the AutoSens in Detroit! AutoSens is the world’s foremost meeting of automotive engineers working to improve automotive imaging and vehicle perception for production vehicles.
Synthetic data to develop a trustworthy autonomous driving system | Chapter 4
The University of Warwick and Anyverse have just started what we hope will be a long partnership in the field of autonomous driving perception systems. Our first joint research project objective is to compare the performance and results of an autonomous driving AI model when training and validating it with real-world data and highly accurate synthetic data.
Acquiring data to develop in-cabin monitoring systems – The challenges
Whether it’s DMS, OMS, or any other interior camera system, acquiring data to develop in-cabin monitoring systems is challenging. But… Why is that? Why is acquiring real-world data particularly hard for the in-cabin monitoring use case?
Synthetic data to develop a trustworthy autonomous driving system | Chapter 3
The University of Warwick and Anyverse have just started what we hope will be a long partnership in the field of autonomous driving perception systems. Our first joint research project objective is to compare the performance and results of an autonomous driving AI model when training and validating it with real-world data and highly accurate synthetic data.
Synthetic data to develop a trustworthy autonomous driving system | Chapter 2
The University of Warwick and Anyverse have just started what we hope will be a long partnership in the field of autonomous driving perception systems. Our first joint research project objective is to compare the performance and results of an autonomous driving AI model when training and validating it with real-world data and highly accurate synthetic data.
A look into the future of data for training and validating autonomous vehicles
If we look back, it’s amazing how many autonomous vehicles and ADAS systems have been developed with real-world data (and will probably continue to be used in combination with more accurate synthetic data), but real-world data limitations are becoming more and more evident, to the point of asking… Will future perception systems need real-world data for training at all?
Can you use synthetic data to develop a trustworthy autonomous driving system? | A Project Logbook
The University of Warwick and Anyverse have just started what we hope will be a long partnership in the field of autonomous driving perception systems. Our first joint research project objective is to compare the performance and results of an autonomous driving AI model when training and validating it with real-world data and highly accurate synthetic data.
How to generate accurate long-range detection data for AV – Facing the challenge
How to generate accurate long-range detection data to train and validate autonomous vehicles has been challenging for developers since the very beginning of autonomous transportation.
Seeking ground truth data generation… not going to happen using human annotators
It may be time to give synthetic data a try, but not just any synthetic data… pixel-accurate synthetic data capable of mimicking the behavior of your self-driving system in the real world.