How automotive assistants will become intelligent co-pilots

Assistants here, assistants there, assistants everywhere.

The tech world seems agog over the idea of building everyone’s new virtual best friend, ready to tirelessly serve our practical needs and whimsical desires anytime and anywhere. After decades of science fiction-induced visions of a future in which intelligent machines with pleasant, reassuring voices effortlessly answer their master’s most pressing questions and blithely fulfill any request, technology has finally reached the point where futuristic fantasies could soon become reality. Some might even argue that soon is right now.

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Planners often talk about how our lives move between three primary environments: home, work (or school) and on-the-go. This makes sense for most of us and has been very useful for product ideation and marketing purposes. It helps creators imagine how their solutions will solve problems unique to each environment.

Automobiles are part of the “on-the-go” scenario for hundreds of millions of people around the globe. It is really quite remarkable how dramatically the automotive industry has been evolving over just the last five to 10 years. Ten years ago, even the most advanced vehicles on the market lacked the intelligence we see hitting the market today. They were mechanical marvels of technology that could perform many impressive functions within and unto themselves, but artificial intelligence (AI), machine learning, true driver personalization, and external data exchange capabilities were still conceptual. Over the last decade, however, driver assistance systems, the internet, and new human-machine-interfaces (HMI) have proliferated in vehicles at all levels in the market. The “connected car” period of the last few years is quickly morphing into the “smart car” era.

The key element to making cars “smart” will be a deep learning AI platform that thoughtfully integrates the car’s HMI with various third-party virtual assistants, vehicle sensors, and off-board content, as well as adapting to user habits and preferences. Smart cars will possess an automotive assistant that can connect a variety of inputs and data sources. Its value will be judged by how elegantly it understands and communicates with its users using speech and natural language, while accessing and delivering a world of information from a wide range of “expert” sources to instantly and/or proactively deliver the right answer, content, or action. In essence, the assistant is agnostic and truly built to assist the driver — and, because it’s optimized for the automotive environment, it comes equipped with expertise about fuel levels and proximity to the nearest gas station, or why that exclamation point on the dashboard is lit up…again.

To be incredibly effective, the automotive assistant must also know and work with the most appropriate sources for any given inquiry and be able to communicate what it learns. In other words, it needs to be interoperable with a wide range of apps, assistants, and platforms that extend beyond the car. In fact, with the sheer number of specialized bots and virtual assistants becoming available as part of the proliferation of the Internet of Things (IoT) across a number of vertical markets — banking, retail, insurance, health care, etc. — an automotive assistant that can communicate and work with these diverse services will drive incredible value for OEMs and consumers alike.

Interoperability is a logical end-state that the full IoT ecosystem will eventually need to embrace if it is to be successful. Assistants and bots will benefit from working together because consumers will ultimately decide they don’t want to be forced to choose — they want to have options and choice on their own terms. One way to think about the relationship between the automotive assistant and its counterparts in the extended mobile realm could be as that of a general contractor and sub-contractors on a construction project. The general contractor may well possess the skills to, for instance, design an electrical system or install plumbing, but their primary role is to manage the overall project to ensure it is completed as efficiently and effectively as possible. To accomplish this, the general contractor will leverage relationships they have with many specialized contractors who can be brought in at the right time to perform specific tasks expertly and quickly. Similarly, the automotive assistant, while highly capable itself, delivers the best experience for users by intelligently coordinating all pieces of the connected world ecosystem.

The automotive assistant greatly improves user experiences using two other very important modern AI advances: “personalization” and “contextualization.” Personalization concerns learning individual users’ particular traits and preferences and using this knowledge to make informed recommendations that better match their needs. Contextualization concerns the conditions and circumstances that surround the user at a given moment — inside and outside the car — because aspects of both might affect the decision for or against a certain option. Your automotive assistant might learn that you treat yourself to fast food on evenings when you leave work, have a client meeting on your calendar, and don’t have enough time to visit your home first. The next time these conditions occur, it may proactively suggest a few selective fast food restaurant options along the route to your meeting, effectively answering the question “What and where can I grab something I want to eat on my way?”

Taken together, rapid advances in AI interoperability, personalization, and contextualization will allow automotive assistants to significantly enhance car mobility for drivers and passengers. They will provide broad access to highly relevant and timely information that will help drivers make better and faster decisions, as well as enhancing the safety and comfort of the car’s occupants.

Bob Schassler is the executive vice president and general manager at Nuance Mobile.