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.
The AI automobile race
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.
Volvo, 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.
Traditional auto eyeing contemporary tech
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. Mobile taxi hailing service, Lyft, partnered with deep-learning startup, Drive.ai, to build AI software for self-driving taxis. International automotive supplier, Delphi, acquired NuTonomy, the autonomous mobility startup, which has been testing self-driving cars in Singapore.
Where there is AI, there is data analytics
Though AI-enabled driver-assist and connectivity features such as sensors, predictive maintenance and geo-spatial mapping, are being integrated into vehicles today, self-driving cars that need minimal human intervention are a different ball-game altogether. In a fully-autonomous vehicle, the driving decisions are governed by AI algorithms that process historical data collected by automobile companies as well as real-time data collated by ML (adaptive learning) systems, which records dynamic road situations and applies them to real-time driving after processing.
McKinsey states that the industry-wide AI-enabled ecosystem for automotive manufacturers will value about $215 billion by 2025. To tap this opportunity, AI and ML technologies need to work in tandem with self-driving and connected vehicle manufacturers. In order to fully integrate these technologies seamlessly and make them mainstream for consumer vehicles, automobile manufacturers need to have strong analytic tools to collate, process and make sense of the data.
Data analytics for autonomous cars is as important as its wheels
AI in auto will create numerous opportunities to reduce costs, improve operational efficiency, optimise pricing, improve maintenance scheduling, predict and match demand and supply as well as help generate new revenue streams. With numerous tech and automotive companies joining the global AI-auto race to leverage these opportunities, it is evident that behemothic amounts of data will be thrown into the mix that may act as productivity roadblocks.
However, a strong and scalable big data analytics platform can put all of it together, streamline the output and increase efficiencies for the entire ecosystem. McKinsey predicts that the big data in cars will become a $750 billion industry by 2030.
For example, present-day data analytic tools are analysing patterns of trains, subways, cabs, automobiles, traffic lights, restaurant traffic and general citizen movement to provide new insights, for preventive measures in autonomous systems. Analytic tools can study sensor data from vehicle fleets, such as travel times and routes and optimise operations to predict the probability of a breakdown and the resulting business impact. Big data analysis from individual cars can help an integrated AI system understand the difference between an accident and a near miss, making future autonomous cars smarter and safer.
Though AI automobiles may never be capable of human intuition, the right data and algorithm will help them make our roads a safer and more efficient way to travel. As fully autonomous cars get exposed to the changing environment, they will become warehouses of data that needs to be constantly processed for actionable insights and performance improvements. The bigger the data the greater the potential for autonomous cars. Hence, self-driving autonomous cars and big data analytics will have to conjointly work to raise the bar for each other.
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Souma Das is Managing Director at Teradata India. He brings with him more than 29 years of technology industry leadership experience that comprises of enterprise software knowledge, general management, sales and business development, strategic consulting and professional services and executive management experience.
At Teradata, Souma is responsible for providing leadership & overall strategic direction to the company’s India business overseeing field operations that include sales, customer management, marketing, professional services and customer support.
Souma is a results-oriented executive who enjoys building, coaching and nurturing teams to create high performing talent driving new growth revenue lines for businesses.
Before joining Teradata, Souma was the Regional Vice President and Managing Director for Qlik for its India operations and was responsible for leading the team to drive growth, revenue and customer satisfaction for organisations leveraging Qlik’s analytics platform.
Prior to joining Qlik, Souma was the Regional Vice President and Managing Director of Infor in India. Souma also, built and headed the Indian operations for Citrix Systems for close to a decade as their Vice President. He started his career with Wipro Technologies and moved on to work for IBM across various roles and functions.
Souma holds a Post graduated in Executive Management from Duke University – Fuqua School business and has a MS in Computer Science and Applications from Jadavpur University.
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