AVOps challenges for generative AI
An organization can face various challenges when implementing an autonomous vehicle operations (AVOps) architecture. Generative AI can help address these challenges:
Scenario coverage: Ensuring that autonomous vehicles are trained and tested in a wide range of scenarios, including different weather, lighting, road conditions, and long-tail/edge case scenarios, is crucial. Generative AI and transformer-based vision foundation models can help enhance and identify edge case scenarios.
As levels of autonomy increase for an automated driving vehicle, the number of complex and unique scenarios that the system needs to handle also increase. Capturing real-world data isn't possible in all situations.
Organizations implementing automated driving solutions need to strictly adhere to industry standards such as ISO 26262. Manual tasks such as requirements or test case management can restrict the implementation process. In addition, there might be major inconsistencies due to the manual nature of these tasks.
The number of lines of code in an autonomous vehicle is approximately a few hundred million. The number of software developers required to work on such a code base is monumental and the industry needs to rethink how software should be developed. In addition, the growth of software in vehicles also leads to an increase in security vulnerabilities, which can affect safety and cost to the original equipment manufacturers (OEMs) and consumers.
Autonomous companies and suppliers deal with enormous amounts of data. So, the ability to search for data requires you to perform time-consuming tasks of labeling even when the data is autolabeled.