By Arpan Chakraborty, Learned Driving @ Ridecell.
TL;DR Driving scenarios provide a robust framework for testing and validation of automated driving systems. This article introduces the fundamental elements and spatiotemporal relationships that characterize scenarios, and incrementally builds an expressive language for representing them.
Driving is commonly described as the controlled operation of a motor vehicle, typically for transportation of people or goods, but often enjoyed as an activity in itself. It requires mastering several skills such as following a lane, merging into traffic, stopping at a red light, yielding to pedestrians, etc. Each of these skills is related to some…
Teams working on autonomous driving collect Petabytes of sensor data over thousands of miles. However, finding valuable events and traffic scenarios from these driving logs remains an unsolved problem.
To address this problem, we launched Nemo — our new Search and Analytics Platform. Nemo (https://nemosearch.ai) is an automated metadata tagging and scenario extraction tool that helps find useful events and traffic scenarios, saving up to 30% of developers’ time, reducing data storage costs by 80–85%, and providing insights to the testing and validation teams on product readiness.
Nemo results from years of research done by Auro, Ridecell’s Autonomous Driving Division…
Search and analytics platform for autonomous driving sensor data