Autonomous cars are as intrinsic to visions of the future as holograms and space travel. Since the birth of science fiction, the automobile has been seen as the final frontier of technological innovation. However, when we look around at our cities today, cars can often seem stuck in the past.
The reality is that the vision for the automotive industry has far exceeded the pace of its progress. That said, estimates project the global autonomous car market will grow from $5.6 billion (paywall) in 2018 to $60 billion in 2030, making it clear that self-driving cars will gain significant ground. In the meantime, artificial intelligence (AI) has slowly begun to transform our vehicles through integrated innovations across car brands.
As the CEO of an AI company focused on high-quality training data, I’ve witnessed firsthand how the automotive industry is harnessing AI in vehicle production. Leveraging these insights and new use cases, let’s take a closer look at how AI is currently impacting and will impact automobiles.
Accident Prevention: Risk Assessment + Driver Monitoring
As AI becomes increasingly accessible to car manufacturers, many vehicle companies have prioritized one central objective: safety.
Tesla, the vehicle manufacturer accelerating the world’s transition to sustainable energy, has been one of the leaders in automotive AI adoption since its inception in 2003. One of the company’s primary innovations is an AI-powered interior camera above the rear-view mirror to improve cabin safety. Leveraging AI innovation, the camera detects and monitors drivers’ eyes to perceive their drowsiness and avoid on-road accidents. The technology builds upon the company’s neural network technology, which analyzes road images to perform object detection and depth estimation. Harnessing high-quality training data constructed from its fleet of nearly 1 million vehicles in real time, the company’s AI effectively warns drivers of nearby risks to avoid collisions.
Like Tesla, it’s essential that all autonomous vehicle manufacturers harness diverse datasets to ensure their AI can enable safe hands-free driving. In addition to well-designed navigation and communications systems, these manufacturers must perfect their technology’s ability to detect, label and react to disparate vehicles, people and objects on the road. The thing is, high-quality AI doesn’t happen overnight.
Rather than trying to get your system right on the first go, it’s essential that manufacturers understand that building accurate, bias-free datasets—and, therefore, AI algorithms—requires a serious investment in human-in-the-loop data training and testing. Fortunately, access to experience training data services has never been more accessible.
As more manufacturers integrate small-scale automation into their vehicles, we’re more likely to pass a semi-autonomous car this year than ever before.
The car someone drives can be an important form of self-expression. To ensure its customers receive a personalized vehicle and driving experience, luxury automobile manufacturer Porsche is now offering new AI capabilities. Through its machine learning-powered configuration system, the “Recommendation Engine,” Porsche suggests vehicle packages based on drivers’ individual preferences.
As with any well-built machine learning algorithm, accurate training data is central to Porsche’s innovation. The company trained more than 270 machine learning models during development to create the most effective recommendations possible. As a result, the AI tool effectively leverages data patterns to predict customer choices. Today, Porsche AI’s recommendations are more than 90% accurate, with accuracy improving with each use and data input.
This trend toward personalization is reflective of a broader social push for a more seamless and efficient purchasing process. In light of ongoing supply chain issues and delays, consumers are becoming less patient when it comes to selecting and receiving their goods. To ensure a high-quality customer experience, car manufacturers can simplify their decision-making process.
In the age of shortages and inefficiency, vehicle manufacturers can also harness well-trained data to create AI algorithms that understand their customers and their respective preferences. With this new technology, I believe we can expect the cars we see on our commutes to become increasingly reflective of the people driving them.
For decades, in-car voice assistance has been relegated to luxury vehicles. Now, as AI becomes increasingly accessible, it’s becoming mainstream.
In 2020, the share of cars with in-car connected services grew to 45% from 30% (paywall) in 2018. Reflective of vehicle manufacturers’ belief in the technology, this number will reach 60% by 2024, with 90% of new vehicles sold globally predicted to have voice assistants by 2028.
In the meantime, the Mercedes Benz User Experience (MBUX) infotainment system remains one of the leaders in in-car voice assistance. Similar to Apple’s Siri, the AI assistant responds to “Hey, Mercedes” and reacts to simple voice commands such as “turn on the radio.”
Historically considered one of the most difficult aspects of AI generation, voice recognition is powered through AI using a combination of Natural Language Processing (NLP) and ML. Trained to interpret driver cues, voice recognition converts human speech into digital understanding.
As we settle into the new year, prepare to see even more AI-assistant-powered cars on the market. Especially as legacy assistants like Amazon Alexa move their way into vehicles from Fiat Chrysler and the new Nvidia Drive Concierge (full disclosure, NVIDIA is a Sama partner) becomes commercially available.
To improve these assistants’ functionality, researchers are continuously working to develop AI that understands language’s many ambiguities and complexities. Thanks to significant advancements in the field due to the accessibility of high-quality data, in-car assistants are quickly powering the introduction of AI into our cars.
With AI investment and development on the rise, technology’s impact on our cars and the overall automotive industry will continue to grow. From helping people choose the perfect car for their families to eventually driving for them, I believe AI’s consumer-facing automotive use cases are some of the most exciting we’ll experience. Through high-quality training data, vehicle brands and manufacturers can stay on the cutting edge of AI innovation, harnessing its capabilities to bring our futuristic vision of cars to fruition.