Five Challenges of Artificial Intelligence for Automotive Applications

What to Watch For When Scaling AI Applications in the Auto Industry

As more companies invest in automotive artificial intelligence (AI) solutions, we come ever closer to the large-scale deployment of a Level 5, fully-automatic self-driving car. Automotive organizations looking to be part of this 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 The challenges facing organizations focused on autonomous vehicles are cumbersome. But being prepared can go a long way. Here are five challenges to anticipate when building and scaling artificial intelligence for automotive applications:

Fundamentals

Teams who are eager to get started often forget to pause and think about the fundamentals. Once you’re ready to engage a data partner to scale your projects, details such as how to get data to your data partner or how you’ll view data from your data partner can get skipped over. Ensure your data partner offers end-to-end support and can offer their expertise and guidance. Once you receive your annotated data, how will you view that data? How will you ensure it meets your quality standards? For example – do you know what program you need to view annotated LiDAR data? If you can’t 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 also may not be tuned in to how the level of complexity can influence their projects. By turning to a reliable data partner, their expertise can help provide 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

Localization is especially crucial within the automotive industry. Because automotive companies need to design artificial intelligence for automotive applications with multiple markets in mind, it’s essential to factor in different languages, cultures, and demographics to properly customize the consumer experience. Localization projects are great to put in front of your data partner who can leverage teams of skilled linguists to develop things like style guides and voice personas (formal, chatty, etc.) and optimize across many languages.

Security

A lot of data collection in the automotive industry contains sensitive data that requires additional security measures in place. 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 crowd and onsite service options, private cloud deployment, on-premise deployment, and SAML-based single sign-on: 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 the high-security standards required by the automotive industry, a critical component in building data-heavy AI solutions.

Retraining

According to McKinsey, one-third of AI products that go live 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 avoided. 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.

Looking Ahead

When it comes to launching world-class AI, the automotive industry’s opportunities are massive – whether you’re working to build smart cars, evolving the customer buying process, or enhancing the in-cabin experience. It’s evident that only a fully operational model that reaches deployment will deliver any kind of business value – 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 currently gradual, we’re hopeful that more and more organizations will leverage substantial amounts of reliable training data to get their AI projects into the real-world. Keeping in mind that world-class AI has to work for everyone, 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 Appen, we’re up to the challenge. Learn more about how we help companies working with the automotive industry.

More Articles Like This

All Articles

Blog

Enhancing AI Efficiency: Streamlining AI Training Data with Workflows 

As the world becomes more and more data-driven, companies are finding it increasingly difficult to manage their ever-growing datasets. Data annotation is a crucial process in many industries, including machine learning, computer vision, and natural language processing. This is especially true for Large Language Models (LLMs), which require massive amounts of labeled text data to learn and improve. As the …  

Blog

Stopping AI Hallucinations in Their Tracks

As large language models (LLMs) with generative AI applications become increasingly sophisticated, there is a growing concern about the potential for these models to produce inaccurate or misleading output.  This issue is known as “hallucination,” where AI models produce completely fabricated information that’s not accurate or true.  Hallucinations can have serious implications for a wide range of applications, including customer …  

Blog

man looking at a computer in an office with a circle to the right that has RLHF and LLM in the circle

The 5 Steps of Reinforcement Learning with Human Feedback

How RLHF Works  Reinforcement learning is revolutionizing the way we approach complex problems in the world of technology and business. It’s a powerful tool that enables machines to learn from their environment and make informed decisions based on rewards and punishments. But what if we could combine the power of reinforcement learning with the human touch?   That’s where the …  

Get in touch with Sales

Join our team!

Join our Crowd

Have a question? We’d love to help.

ImageImage
Join Our Crowd
@ 2023 Appen Limited
Legal Policies
Data Subject Access Request
@ 2023 Appen Limited
Legal Policies
Data Subject Access Request
Website for deploying AI with world class training data
Language
Chinese
Japanese