Where to Focus Automotive AI Investments: In-Cabin Experience

Automotive AI as an Instrument of Change and Profitability

Never before has it been more critical for automotive companies to invest in artificial intelligence (AI) solutions. Tractica forecasts that the market for automotive AI hardware, software, and services will reach $26.5 billion by 2025, up from $1.2 billion in 2017. The potential for change is tremendous, with shifts from traditional to disruptive models in the industry no longer unusual, but expected. Automotive companies are seeing AI as a new engine of profitability for the sector.

As the race to a fully autonomous vehicle continues, a standard has been put into place to define six levels of automation so that automakers, suppliers, and policymakers can discuss and compare systems. These six levels of automation correlate to different consumer experiences, with a pivotal shift occurring between levels two and three. This is when AI, and not the driver, becomes responsible for monitoring the vehicle. Regardless of when full autonomy is achieved, advances in the in-cabin and out-of-car experience at all levels are valuable, particularly with respect to enhancing the customer experience and providing quick wins to companies looking to scale.

levels of autonoumous vehicles for automotive ai

Automotive companies that leverage AI for both in-cabin and out-of-car experiences are likely to see considerable returns on investment as consumers clamor for increasingly personalized experiences and as the market for AI-powered solutions grows. For part one of this two-part series, we’ll focus on the key areas of opportunity in the in-cabin experience.

An AI-Powered Cockpit

The in-cabin experience is often described as the AI-powered cockpit but goes beyond just the driver’s experience. In-car experiences encompass the entire user experience – including the driver and all passengers, working to make the overall in-cabin experience more intelligent and enjoyable. This might include AI uses for intelligent driver assistance programs that improve safety or infotainment systems that can provide directions for the driver while giving content recommendations for those in the backseat.

The AI-powered cabin has become synonymous with many companies’ brands. While studies show customers make their first purchase of a particular car brand based mostly on outside appearance characteristics, their likelihood to become a repeat customer is primarily driven by the interior of the vehicle. In-cabin AI, and the experience it enables, is possibly one of the most effective levers in driving customer loyalty. Auto manufacturers are partnering, or looking to partner, with relevant ecosystem providers to create more value for their customers. Benefits of the AI-powered cabin include improved driver experience and safety, as well as intuitive in-car assistants.

Staple elements of automotive AI that powers a smart cockpit consist of object classification, scene understanding, location services, natural language understanding, face detection/fingerprint recognition, sensor processing and fusion, voice/noise cancellation, and voice/speech recognition. These come together to create AI-powered in-cabin experiences like:

Conversational Assistance

Conversational assistance enables consumers to interact with their vehicle in a 21st century way – through speech. Conversational assistance is not only more convenient for the driver, but also much safer than navigating a smartphone user interface (UI) while driving. Your data partner should provide comprehensive training for conversational AI, including speech collection, speech annotation, transcription for the creation of Automated Speech Recognition models, lexicon building, and more.

While voice is clearly the UI of the future, it requires extensive training to be usable by people of different accents and languages. For example, a native English-speaking male driving a car manufactured for the U.S. market will likely experience a much higher level of speech recognition success than a woman or non-native speaker. In simple terms, speech recognition systems that rely primarily on data collected and annotated based on native English male voices will not work as well for other voices. Bringing in natural language understanding to tune models for over 180 languages and dialects will improve the AI experience for everyone.

Driver & Passenger Monitoring System

In-cabin AI can also include driver monitoring systems, which help ensure drivers are paying attention to the road and are capable of safely operating the vehicle. Passengers can be monitored as well, not only to make their in-cabin experience better, but also to make sure they don’t pose a distraction for the driver. Your data partner should support your tracking models through a vast set of multimedia data such as facial and gesture annotation to ensure AI-models remain unbiased. For example, if the training data for a driver monitoring system is based on data collected in an environment with one quiet passenger, the system will respond poorly when monitoring a car full of a large family and their favorite pet.

Unbiased Training Data Will Power Success

An AI model is only as good as the data it’s trained on. A model trained by an engineering team in Germany, for example, might not be as effective at monitoring drivers in Japan. This is why it’s important to have a robust dataset representative of the people your AI needs to be compatible with.

Ensuring enough unbiased training data for multimodal and multimedia visual and speech recognition systems requires an enormous number of diverse annotators representing a wide range of geographies, cultures, and genders. And all this data must be collected, annotated by human experts, and used to train and improve ML models efficiently, with speed, and at scale.

This is no easy task: accessing quality training data across the spectrum is difficult. Yet, unbiased training data is an essential component for automotive AI. Especially for companies looking to win the autonomy race. Companies that invest up-front in an unbiased, quality data solution should expect higher returns down the road as the AI market expands.

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