Data Analytics: A Prerequisite to Artificial Intelligence Mobility
The line between present and future is blurring in the automotive industry at the hands of a paradigm shift in vehicle technology. Self-driving and artificially intelligent automobiles are no longer future concepts. We are no longer envision the idea of safe mobility, security, environmental protection, driving pleasure and convenience, for the “next-decade,” rather this will be driving our lifestyles in the next two-to-three years. Automobiles today are not only machines on wheels, but an inseparable communion of software and hardware. Features like cruise-control, driver-assist, anti-collision-systems, geolocation, connectivity integrations among others, have crept into the mass market; enhancing vehicle safety, comfort and convenience. However, the industry’s goal is to make vehicles an extension of humans, rather than an accessory for humans. And this is where artificial intelligence (AI) and machine learning (ML) come into play.
AI seems to have kickstarted a race in the automotive industry. Companies such as Tesla, Ford, Volvo, BMW, Audi and Mercedes are a few front runners that have moderately integrated AI and ML into their present-day automobiles, aiming to produce fully automated cars in the next 3-5 years. Take Tesla for example, as one of the pioneers in the electric car industry, its autonomous driving features and software performance upgrades, like smartphones, are changing the perception of tech-innovations in the sector. the company known for its safety features, has tied up with business analytics solutions company, Teradata, to launch a system that takes preventive action by predicting car-component failures beforehand.
This allows the company to better plan for its parts inventory. Reduced inventory means reduced costs and a more efficient supply chain, which in turn means customer satisfaction. Volvo states that 80-90% of its new cars are ‘connected’ (post customer permissions) to gather data-driven information about driving behaviour, car related warranty data, customer data and reactions on the road. All to enhance current models and develop future models that interact seamlessly with the consumers.
Volvo Cars uses Teradata analytics solutions and the Internet of things (IoT) to enable advanced analytics for key initiatives such as Volvo Cars Autopilot, Vehicle to Vehicle Communication and Project 26. For example, sharing information about road conditions, collected by several connected Volvo cars, can in the close future be shared with other cars and with road-maintenance authorities.
We are seeing an era where new tech-players are becoming important partners for traditional automotive companies. For example, the American car manufacturer, Ford, invested $1 billion in Argo AI to develop self driving cars. BMW acquired computer vision company, Mobileye, to integrate ML in cars by 2021. Audi and Mercedes are also extensively using AI to automate present-day car functions, and aim to produce fully-automated vehicles in the next few years.
Interestingly, non-automotive companies have also joined the brand-wagon for exploring AI and ML solutions for vehicles. For example, Google’s self-driving car project, Waymo’s partnership with Fiat-Chrysler has been shuttling passengers in Phoenix, USA for over a year.
