I had the pleasure to be invited by Canonical’s AI/ML Product Manager, Andreea Munteanu, to one of the recent episodes of the Canonical AI/ML podcast. As an enthusiast of automotive and technology with a background in software, I was very eager to share my insights into the influence of artificial intelligence (AI) in the automotive industry. I have a strong belief that the intersection of AI and cars represents a pivotal point where innovation meets practical implementation, and leads to safer, more efficient and more user-friendly cars. 

In the episode, several key issues in the use of AI in cars and automotive in general came up. It’s not just the use of AI that we should be thinking about, but a whole range of safety, ethics, and privacy concerns that can eclipse simple technical challenges. This underscores the importance of considering the broader societal impacts and ethical implications of integrating AI into automotive technologies.

This blog explores the key takeaways from the engaging conversation we’ve had, diving into the present and future implications of AI in the world of automobiles. We talked about a lot in the half-hour discussion, but a stand-out moment for me was when we spoke about the impact AI implementation has on costs. I’ll get more into why I thought this was the most important part of our discussion in a bit, but for now you can listen to the entire conversation yourself in the podcast episode.

AI is everywhere in automotive

AI is already embedded in every aspect of the automotive sector. This key role is not just limited to autonomous vehicles: AI is integral to manufacturing processes, predictive maintenance, and supply chain management. In almost every part of the automobiles – whether it’s conceptualising and building cars, driving them, or monitoring their performance throughout their lifecycle – AI is critical.

Safety considerations

Cars driving themselves around makes people very nervous, especially when algorithms are tasked with making intricate split-second decisions that boil down to “don’t swerve into oncoming traffic”. It’s no surprise that safety is the paramount factor in vehicle AI conversations. Therefore, it is imperative to address the safety concerns associated with the integration of AI in automotive technology.

“Would you protect the driver and the vehicle occupants versus all the surrounding pedestrians? In some cases, the vehicle will have to choose”*
Bertrand Boisseau

It’s a troubling ethical concern: do machines have a right to make decisions about human life, and what are the limits to that decision-making process? AI and autonomous vehicle engineers have their work cut out for them, as these decisions are incredibly complex and happen at the speed of life. When a glitch happens on your desktop, it’s not so bad because you’re not travelling at 100 km/hr through 2-lane traffic with oncoming trucks and pedestrians on every side.

While these challenges are significant and lead to a lot of uncertainty about whether it is safe to let Autonomous Driving (AD) vehicles drive around at the maximum speed limit, we should pause for a second to reflect on the extreme and ongoing testing and retesting that they undergo. 

Driverless cars often make headlines when accidents happen. But it’s important to remember that accidents are part of driving, whether it’s with a human or autonomous tech. In reality, driving carries risks, and you’re likely to get in a car accident in your lifetime. So, while one accident might spark concerns, it’s crucial to see it in the bigger picture of transportation safety. 

Also, a study comparing human ride-hail drivers and self-driving cars in San Francisco revealed that human drivers are more likely to crash, cause crashes, and injure others than autonomous vehicles. Human drivers had a crash rate of 50.5 crashes per million miles, while self-driving cars had a lower rate of 23 crashes per million miles.

Additionally, the development of robust fail-safe mechanisms and redundant systems can serve as safeguards against potential algorithmic errors or malfunctions. Furthermore, ongoing collaboration between industry stakeholders, regulatory bodies, and research institutions fosters the establishment of comprehensive safety standards and guidelines for the integration of AI in automotive technology. 

By prioritising safety considerations and adopting a multi-faceted approach encompassing technological innovation, rigorous testing, and regulatory oversight, the automotive industry can effectively address the safety challenges associated with AI integration, paving the way for safer and reliable autonomous driving systems.

Diverse applications beyond driving

While self-driving cars often take centre stage, AI solves a broader spectrum of problems for the automotive industry: optimising manufacturing processes; predictive maintenance for parts replacement; and enhancing supply chain management efficiency, to name a few. It will also transform the in-car experience with advanced voice recognition and personalised assistance.

“I do believe that having advanced personal assistant will be noticeable for the user. Once you start putting voice recognition in there, it can become, I think, very useful.”*
Bertrand Boisseau

Challenges and concerns

On the podcast, we mention that safety is the most obvious concern when it comes to the use of AI in cars, but there are even greater challenges and concerns that developer automotive industry figures should be thinking about. These include privacy issues, the role of regulation in the use of AI, public trust in AI systems, job displacement fears, and the substantial costs associated with running AI/ML models, both in terms of processing power and energy consumption. 

“You want to make sure that whatever is sent to the training models still complies with data privacy concerns: how do you collect data, how do you share vehicle data -which is usually private data-, how do you train these models?”*
Bertrand Boisseau

When it comes to training machine learning models for autonomous vehicles, maintaining data privacy is crucial. We need to be mindful of how we collect and share vehicle data, ensuring it aligns with privacy concerns. It’s vital to gather data ethically and responsibly, while also validating its quality to prevent biases and inaccuracies. After all, if we feed the models with flawed data (from bad drivers, for example), we risk compromising their performance and safety. So, robust data validation processes are essential to ensure the effectiveness and reliability of autonomous vehicle technology.

The evolution of jobs

As AI evolves, so too do the nature of jobs in the automotive industry. Take developers as an example: as AI gains a stronger foothold in automotive development, our roles will transform from manually coding algorithms to focusing on simulating and validating AI models. 

“I don’t agree with the idea of having job displacement in any way, but I do think that there is going to be a shift [in] the market, and there is a clear skill gap or understanding gap.”*
Andreea Munteanu

The industry faces a growing need for individuals with expertise in both AI and automotive engineering, bridging the gap between technology and traditional automotive skills.

However, it’s also crucial to acknowledge the widespread concerns about the potential impact of autonomous vehicles on various job sectors within transportation, including taxi drivers, delivery drivers, truck drivers, valets, and e-hailing service contractors. While autonomous technology is advancing rapidly, broad legislation still typically mandates the presence of a human driver to take over the wheel if necessary, meaning fully human-free cars aren’t imminent.

The use of open source

Open source software will play a key role in the automotive sector. Open source software presents indispensable advantages such as unparalleled transparency, enabling thorough inspection and auditability of the codebase. 

“Open source software in general and even [especially] in AI/ML would be the wiser choice in most cases.”*
Bertrand Boisseau

This transparency not only fosters trust and reliability but also empowers developers to identify and rectify potential issues swiftly, ensuring the highest standards of quality and security. Additionally, going with closed source might mean that Original Equipment Manufacturers (OEMs), or even the customers, have to pay extra fees per year just for licences. Imagine having a “smarter” car that becomes useless if a licence lapses or expires. Open source cuts down on these costs since you’re not constrained by licences, making software cheaper to create, keep up, and expand. Fewer closed source licences mean less complexity in the user experience.

The adoption of open-source models, tools, and frameworks is likely to grow, especially as companies aim to balance innovation and security.

Data privacy

As AI becomes increasingly integrated into the automotive industry, ensuring robust data privacy measures is paramount. The vast amounts of data generated by connected vehicles, ranging from driver behaviour to location information, raise significant privacy concerns. 

It’s essential to implement strict and clear data protection protocols to safeguard sensitive information from unauthorised access or misuse. Additionally, transparent data collection practices and clear consent mechanisms must be established to ensure that users have control over their data. 

Failure to address data privacy issues adequately not only risks violating privacy regulations but also erodes consumer trust, hindering widespread adoption of AI-driven automotive technologies. With the implementation of EU policies such as GDPR, fines can be as high as 10 million euros or up to 2% of the company’s entire global turnover of the preceding fiscal year (whichever is higher), further emphasising the importance of robust data privacy measures.

AI can reduce costs in automotive

Cost considerations are another crucial aspect of integrating AI into the automotive industry. While AI technologies hold immense potential to optimise operations, enhance safety, and improve the driving experience, they often come with significant upfront and ongoing costs. 

The automotive industry is also fiercely focused on cost optimisation: cars that are more expensive are a severe risk for sales, especially in saturated markets. What good is AI and all the hardware and infrastructure it will need if it just leads to cars that their usual buyers can no longer afford? 

Additionally, ensuring compatibility with existing systems and regulatory compliance may incur other expenses. Moreover, there are ongoing costs associated with maintaining and updating AI systems, as well as training personnel to effectively use and manage these technologies. 

However, despite the initial investment, the potential long-term benefits, such as increased efficiency, reduced accidents, and improved customer satisfaction, can outweigh the costs over time. Therefore, while cost is a critical factor to consider, automotive companies must carefully weigh the upfront investment against the potential long-term returns and strategic advantages offered by AI integration.

Regulations: the wild west won’t stay wild forever

Navigating regulatory frameworks generally presents significant challenges. This is already true for the integration of AI into the automotive industry. Regulators are often slow to react to the rapid pace of technological advancements, resulting in a lag between the emergence of new AI-driven automotive technologies and the establishment of appropriate regulations. This delay can create uncertainty and hinder innovation within the industry as companies navigate ambiguous regulatory landscapes. 

However, once regulatory wheels are set in motion, they can hit like a truck, with stringent requirements and compliance measures impacting the entire automotive ecosystem. The sudden imposition of regulations can disrupt ongoing projects, necessitate costly adjustments, and delay the deployment of AI technologies. 

Therefore, automotive companies must remain vigilant and proactive in engaging with regulators, advocating for clear and forward-thinking regulatory frameworks that balance innovation with safety and compliance. By fostering collaboration and dialogue between industry stakeholders and regulators, the automotive industry can navigate regulatory challenges more effectively and ensure the responsible and sustainable integration of AI technologies.

Reconciling AI and sustainability

Sustainability and energy consumption are crucial topics of debate in the automotive industry, especially concerning the integration of AI technologies. Data centres, which are essential for processing the vast amounts of data generated by AI-driven systems, consume substantial amounts of energy. The energy usage of a single data centre can be equivalent to that of a small town, highlighting the significant environmental impact associated with AI infrastructure.

“If you need processing power, you need energy. The big [AI/ML] players have also been saying that we will need to build nuclear power plants to run all the requests.”*
Bertrand Boisseau

Similarly, badly optimised, individual autonomous cars, with their sophisticated sensor systems and computational requirements, might also consume considerable energy during operation.

As the automotive industry embraces AI, it must address the sustainability implications of increased energy consumption and explore strategies to minimise environmental impact, such as optimising algorithms for efficiency, utilising renewable energy sources, and implementing energy-saving technologies.

Addressing criticisms of automotive automation

Automation in the automotive industry presents significant potential, yet it’s essential to address ongoing discussions surrounding the broader concept of automation, particularly in social media and consumer circles. Questions arise, challenging the value of autonomous driving and whether every aspect of a car’s operation needs to be automated. While these debates hold merit, they often overlook the broader implications and benefits that automation can bring.

Arguments against automation often highlight concerns regarding the potential loss of manual driving skills and the ability to react to unforeseen situations beyond the scope of automated systems. However, it’s crucial to consider that historical transitions in automotive technology, such as the shift from manual to automatic transmission or the adoption of adaptive cruise control, have not resulted in increased accidents — quite the opposite, in fact. On top of that, the advancement of automation extends beyond driverless vehicles alone, encompassing a multitude of frameworks, optimisations, and breakthroughs with far-reaching impacts.

Drawing parallels to other technological achievements, such as the space program, sheds light on the extensive benefits that arise from ambitious projects despite initial scepticism. Much like criticisms were raised against space exploration, which questioned its necessity or deemed it a misallocation of resources, the collective efforts in the automotive industry toward automation yield a number of innovations and enhancements. These advancements not only streamline operation and maintenance but also significantly enhance safety for drivers and road users alike. Therefore, while discussions surrounding automation provoke diverse perspectives, embracing its potential fosters progress and innovation within the automotive landscape, and beyond.

The future of AI in automotive

In the future, AI in the automotive industry will certainly be widespread; but the application of AI will dominate more specific use cases, such as autonomous driving systems, personal assistants or predictive maintenance. The reasons for this are quite simple: the data processing and warehousing for each automated vehicle become difficult to design and expensive to run, especially when the financial returns on AI products and their long-term financial sustainability are still unproven. There are still strong challenges when it comes to generating revenue from AI investments, particularly in the automotive realm, where return on investment and sustainable business models are still evolving.

I found our podcast conversation on AI in the automotive industry incredibly engaging, especially when we delved into the potential impact on safety and driving experiences. It’s fascinating to envision how AI will revolutionise not just the way we drive, but also how vehicles are manufactured and maintained. As AI paves the roads of tomorrow, the integration of AI into the automotive industry promises a transformative journey.

As a passionate car enthusiast, I think we’re headed towards a new era of innovation. AI will be in our cars, homes, jobs, buses, and perhaps even our law-making offices. As it grows and evolves, it’ll be even more important to keep track of its progression and adoption – which is why I’m glad that podcasts like ours exist. If you want to stay ahead of AI/ML and GenAI in the automotive industry – or indeed, any industry – and watch its interplay with open source applications, follow the Ubuntu AI Podcasts by Canonical.

*quotations edited for clarity and brevity

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Further reading

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