The self-driving market needs to reach the level of development where a driverless vehicle is 100% safe (and perceived as such by society) and reliable to operate like another manned vehicle in the vastness of the real world. In order to achieve this, one of the biggest challenges must be appropriately dealt with: the performance of autonomous driving in adverse weather conditions. How are you going to gather the data to achieve this?
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 to train and validate autonomous vehicles has been challenging for developers since the very beginning of autonomous transportation.
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.
If developing and validating autonomous driving systems wasn’t already hard enough… having inaccurate data could make your life even harder.
Learn everything you need to know on why data accuracy is key to generating data for autonomous driving training, testing, and validation