Top 10 ways AI is impacting Mobility
How important is Artificial Intelligence? Very. AI is powering a Fourth Industrial Revolution, its transformative force similar to the drivers of the previous three (steam, electricity and computing). Thanks to AI, objects are getting ‘agency’: they won’t need us anymore to make decisions and take action. Mobility is arguably the ecosystem where AI’s tremendous potential is most visibly emerging. The value of AI in Automotive is expected to approach €10 billion by 2024. What is that money being spent on? Here are our Top 10 ways in which AI is changing Mobility.
10. Personalised marketing
Been googling for hiking boots or fishing gear? Your infotainment system will point out nearby outdoor stores. Is your tank or battery running low? Your car will suggest refuelling or recharging stations. Love Thai food? Your car knows a great place, just around the corner… Of course, those stores will pay for the privilege of your custom. Welcome to personalised marketing on wheels, thanks to AI.
Both the insurance industry and AI are big on predicting the future. That overlap is why AI’s deep learning capabilities have given rise to a whole new approach to insurance: it’s called insurtech, short for ‘insurance technology’. Examples include: driver risk assessments, based on risk factors filtered out of Big Data; and DIY insurance apps, enabling drivers to file their own damage assessments after an accident (via video apps like China’s Dingsunbao 2.0).
AI is not just changing the way cars are driven, but also how they are built. Kia, for example, recently introduced the Hyundai Vest Exoskeleton (H-VEX), a wearable robot for assembly lines both protecting workers and making them stronger. And then there are Automated Guided Vehicles (AVGs) are moving around materials where needed without human intervention. Painting and welding robots identify defects and adjust their processes accordingly. All these AI-guided robots will make car manufacturing safer, more efficient and less costly.
7. Predictive maintenance
By closely monitoring hundreds of sensors and analysing them to evaluate a vehicle’s current state, AI can fairly accurately predict upcoming vehicle malfunctions. In short: AI detects problems before they affect driving and suggests maintenance before the issue leaves drivers stranded and/or vehicles damaged. Partnering with Microsoft, Volkswagen will soon offer over-the-air (OTA) updates for predictive maintenance.
Thanks to the advanced natural language abilities of AI, it will become easier to operate the in-vehicle infotainment systems, via simple voice commands and hand gestures – as for example via the digital AI assistant developed by Berlin-based startup German Autolabs. This enhances the comfort and safety with which drivers can operate the messaging, navigation and entertainment functions of their vehicle.
5. Smart grid management
With an electric car, it makes sense not only to charge its battery when electricity rates are lowest, but also to plug it in when its stored power can be resold to the grid at the highest rate. AI will predict the best times to charge your EV, and to use it for vehicle-to-grid (V2G) ‘un-charging’. This ‘smart grid management’ not only reduces cost for the EV user, but also increases the stability and efficiency of the entire grid itself.
4. Driver monitoring
Not only will AI software be able to identify individual drivers – adjusting vehicle settings to their preferences (seat position, temperature, mirrors, etc.) AI (as developed by Israeli startup eyesight, for example) is also able to monitor the driver’s capacity to operate their vehicle, by measuring eye openness, head position, and other indicators of alertness. If necessary, the system warns the driver to regain focus, or take a break. In case of accident, posture management allows the best possible deployment or airbags to reduce injury.
One or two steps short of full autonomy, AI is already contributing to taking the human effort out of driving. Increasingly sophisticated Advanced Driver Assistance Systems (ADAS) are turning AI into an ever more important co-pilot, helping vehicles maintain lanes and distances, helping with emergency braking parking in tight spots, monitoring cross-traffic and blind spots, and even taking over control to avoid accidents.
2. Traffic forecasting
AI is really good at extracting useful information from Big Data, such as info on traffic conditions at certain dates, days and hours. This allows it to plot a trajectory that will ‘beat’ the traffic by avoiding congestion where it is most likely to occur. Similar machine-learning functionalities can be applied to freshly emerging traffic incidents, providing the driver with the best options to avoid not just unexpected traffic jams, but also the alternative routes most likely to clog up by displaced traffic.
1. Full autonomy
Full, level-5 autonomy remains the Holy Grail of the multi-billion-dollar R&D effort around self-driving technology. AI is a large part of that effort, and the eventual breakthrough of full autonomy is largely dependent on progress in machine learning. Two players are ahead of the pack when it comes to full-autonomous driving: Google (via its subsidiary Waymo) and Tesla. Both are conducting self-drive experiments that should result in limited-range tests, which will probably be closed-range (e.g. bus routes rather than taxi rides) for the foreseeable future.
Predicting the arrival of the full-autonomous car for general use is risky business, but it’s very possible that it will involve either of these two companies. And it will certainly involve a generous helping of AI – the driver of the Fourth Industrial Revolution.