Gathering data for autonomous driving in adverse weather conditions

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?

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.

Client Story

Would you like to know how Cron AI has improved LiDAR simulation accuracy with physically correct synthetic data?

Let's talk about synthetic data!

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