What to Watch for When Deploying AI Applications for Autonomous Vehicles
As more agencies invest in automotive artificial intelligence (AI) solutions, we come ever closer to the large-scale deployment of Level 5, fully autonomous, vehicles. Organizations looking to be part of this automotive AI disruption should already be thinking ahead about how to deploy their artificial intelligence for automotive applications successfully and at scale. To start, they should be aware of and prepare for several key challenges. Doing so will help build in safeguards to keep forward momentum.
The Challenges Ahead with Artificial Intelligence for Automotive Applications
AI for Automotive Applications
Autonomous vehicle initiatives face numerous potential points of failure. But being prepared can go a long way. Here are five challenges to anticipate when building and scaling artificial intelligence for automotive applications:
Teams eager to get started often forget to first consider the fundamentals. Once you are you are ready to engage a data partner to scale your projects, details such as how to get data to your data partner or how you will view data from your data partner can get skipped. Ensure your data partner offers end-to-end support with a strong repository of expertise and guidance. Once you receive your annotated data, how will you view it? For example – do you know what program you need to view annotated LiDAR data? If you cannot view this data, how do you ensure it was done correctly, and the project was adequately annotated so your models can properly leverage the data? A good data partner will be able to offer support through every phase of your project from start to finish.
Level of Complexity
Like the fundamentals, organizations may not be tuned in to how the level of complexity can influence their projects. By turning to a reliable data partner, their expertise provides direction and insight. The larger the ontology, for example, the more complicated the project. A well-versed data partner will help identify how this leads to more time and cost and find solutions that will work for your overall business objectives, which is especially critical for factoring in images and videos.
Localization is especially crucial in automotive. Autonomous initiatives need to design their AI with multiple markets in mind; it is essential to factor in different geographies, understand military jargon and agency specific acronyms, identify US and coalition partner vehicles, markings, and uniforms, and operate in a variety of climate and environmental conditions (for example, desert, arctic, tropical, etc.).
A lot of information collection in the automotive industry contains sensitive data that requires additional security measures. A proper data partner will offer a variety of security options and have strong security standards at even the most basic level to ensure your data is handled correctly. Look for data partners who offer options such as secure data access (critical for PII and PHI), confident and qualified crowds, and onsite service options.
Secure Data Access ensures all data security requirements are met for customers working with personally identifiable information (PII), protected health information (PHI), and other sophisticated compliance needs.
Secure crowd and secure onsite service options where contributors access tasks through machines that are owned/operated by the channel in a controlled and monitored physical location.
Private cloud deployment can be hosted on your specific cloud environment or hosted and managed by your data partner.
On-premise deployment deployed in your particular network, either air-gapped or non-air-gapped.
SAML-based single sign-on (SSO) gives members access to the platform through an identity provider (IDP) of your choice.
A data partner who offers the above options will likely meet your high-security standards, a critical component in building data-heavy AI solutions.
According to McKinsey, one-third of AI products in production need monthly updates to keep up with changing conditions, like model drift or use case transformation. Many companies skip over this critical step or put it on the back burner altogether. Still, the risk of your AI project deploying at scale and being successful long enough to prove ROI becomes increasingly limited the longer retraining is postponed. Retraining allows you to iterate on your model, making it more accurate and successful – this is best done by leveraging a data partner for relabeling data and providing support by using human evaluators to analyze low-confidence predictions.
When it comes to launching world-class AI, the automotive project opportunities are endless – whether building Next Generation Combat Vehicles (NGCV) Future Vertical Life (FVL) or the evolution of mail delivery. It is evident that only a fully operational model that reaches deployment will deliver successful outcomes – and the best way to beat the less-encouraging odds is to address expected data and AI challenges ahead of time and to identify use cases where reliable training data (with the right data partner) can get you there.
While the path to an AI-driven automotive revolution is gradual, it is evident that more and more organizations are leveraging substantial amounts of reliable training data to get their AI projects into the real-world.
Keeping in mind that for world-class AI to work for in every market, attention to up-to-date localization, data security, and the removal of bias from data is paramount so that AI recognizes everything and everyone equally. Organizations that embrace this concept and invest in a reliable data partner are likely to come out ahead as the race for full automation continues. At Figure Eight Federal, we are up to the challenge. Schedule a demo to plan how to make your AI automotive projects move from Pilot to Production.