Self-driving Not The Only Frontier of Innovation In Automotive, Check Out Other Use Cases Of AI
While self-driving cars are gradually becoming a reality, there are more applications of AI and machine learning that the automotive industry is heavily investing in and are probably going unnoticed. Over the years, automotive companies are broadening the scope of artificial intelligence and deep learning technologies beyond autonomous vehicles and into other niche areas of business, such as in-user experience, predictive maintenance, gesture recognition, intelligent auto insurance and more. According to a new report from Tractica, both semi-autonomous and the fully autonomous vehicles of the future will rely heavily on AI systems. And AI is no longer restricted to autonomous driving, revealed Tractica’s principal analyst Keith Kirkpatrick. It is this renewed focus on new use cases that will push AI hardware, software revenue to $14.0 billion by 2025. Hence, self -driving is not the only frontier of innovation AI is knocking on in the automotive industry. AI is helping bring more operational and business transformation in the automotive industry, with an increased level of accuracy in performance Analytics India Magazine lists down popular AI-powered use cases from the automotive industry that go beyond self-driving Driver Face Analytics and Emotion Recognition: Increasingly, automotive companies are looking for ways to use driver’s emotion data to better understand the user and deliver a more personalized in-car experience. So, how will Emotion AI win customer loyalty and driver sales? An emotion-enabled vehicle will make it easier to monitor the user for drowsiness, fatigue and any distraction which can help in preventing accidents. Integrated with weather and traffic data, Emotion AI can also deliver highly personalized recommendations concerning routes and even audio system. Emotion AI technology is developed by Affectiva, an MIT spin-off and is being integrated into Advanced Driver Assistance Systems and supplier technology stacks in both autonomous and non-autonomous cars. Japanese automotive giant Honda has taken the first tentative step towards building an emotionally-aware car that will definitely win over customers. Localization and Mapping: A key technology for autonomous driving is the real-time high-definition (HD) map. So far, maps in cars are mainly used for navigation purpose and aren’t precise enough for autonomous driving. For fully automated driving, one requires an intelligent control system that comprises of sensors and robotic technologies. For a fully automated car, it is important to identify, detect and avoid obstacles and the current maps do not offer the right resolution or sufficient information to navigate traffic, especially in high density areas. Autonomous driving requires high-precision localization and it’s here that HD maps help predict appropriate behavior in traffic that beyond the sensors of a robotic car. By mapping HD map data with key landmarks as reference positions, carmakers can deliver accurate and real-time information. Intelligent Auto Insurance: AI is also set to change the auto insurance industry by helping automotive companies find out risky drivers and assess claims damage appropriately. Finding out risky drivers will help significantly in fleet management for cab aggregator companies such as Lyft and Uber that can monitor the drivers and help them manage their vehicles effectively. Solaria Labs, the tech arm of Boston-headquartered Liberty Mutual Insurance company has developed an AI Auto Damage Estimator that helps drivers gauge the repairs needed and the estimated cost after the damage has occurred. All this information is made available in real-time and the damage is assessed by comparing damage to the thousands of car crash photos uploaded on the app. Gesture Recognition:  Carmakers are definitely paving the way for next-gen technology, moving beyond touchscreen to gesture recognition and eye movements through which you can switch between radio channels in the car and even control in-car temperature. The central premise is – poking  a touchscreen can be distracting for a driver and gesture recognition could very well add to the voice and touch control. The technology uses a camera and proximity sensors to assess hand patterns for certain pre-programmed functions. The technology is integrated in the BMW 7 Series that recognises six hand movements waved in front of its centre console. This tech marvel is expected to be integrated in other BMW models as well. Outlook With a shift towards autonomous vehicles, cars will soon become a treasure of consumer data and will present several opportunities to automakers and OEMs for car data monetization based on specific user data related use cases. The new automotive business model will be one built on customer data and there will be several, key stakeholders involved (service providers, OEMs, dealers, law enforcement authorities and more). According to a McKinsey estimate, up to 15 percent of passenger vehicles sold in 2030 would be completely autonomous. This will also bring in sharp focus the data privacy concerns around location data, navigation data, vehicle data and the risks associated with it, such as hacking and misusage of data. Keeping this in mind, automakers and other stakeholders will also have to build a reliable data management as one of the core capability.
While self-driving cars are gradually becoming a reality, there are more applications of AI and machine learning that the automotive industry is heavily investing in and are probably going unnoticed. Over the years, automotive companies are broadening the scope of artificial intelligence and deep learning technologies beyond autonomous vehicles and into other niche areas of […]
Analytics India Magazine lists down popular AI-powered use cases from the automotive industry that go beyond self-driving
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["Go", "machine learning", "artificial intelligence", "AI", "innovation", "BERT", "Aim", "deep learning", "analytics", "R"]
["AI", "artificial intelligence", "machine learning", "deep learning", "analytics", "Aim", "R", "Go", "BERT", "innovation"]
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.