AI & Automotive — 8 Disruptive Use-Cases
A driverless car running on roads may sound like a screen taken from a sci-fi movie. However, fiction is turning into reality, and we thank Artificial Intelligence (AI) for the same. AI technology complements the very concept of self-driving cars.
Elon Musk had predicted in 2017 that all the cars will be autonomous in 10 years without any steering wheel. We are quite close to bringing this prediction into reality in just a short time frame of 4 years.
Mercedes-Benz, Volvo, Bosch, Nissan, General Motors, and Continental Automotive Systems, among others, are some of the automotive players striving to gain the first-mover’s advantage in the market through the development of autonomous cars, using the prowess of AI.
Advancements in AI have an immense contribution to the growth of the automotive industry. By 2025, AI will reach an annual value of $215 Billion in the automotive industry, and will move to become a mainstream trend from here on. The United States is currently the largest automobile market worldwide, both in terms of production as well as sales.
The installation of AI-based systems will rise by 109% in 2025. Major players like Audi, Tesla, Hyundai, Benz, Nissan, Kia are striving hard to incorporate AI, automate the cars and are looking ahead to developing tools that can help with the installation of self-sufficient cars.
AI & Automotive — Use Cases
At a high level of abstraction, the following are the ripples of disruptions caused by AI in the automotive industry.
Use Case 1 — Design:
Right from the brainstorming part to developing a working car model, the design and development engineers consider numerous aspects to be as consumer friendly as possible.
Here are a few use cases showcasing how OEMs are using AI to speed up their design workflows:
- Nvidia’s architecture uses AI, real-time ray tracing, and programmable shading to transform the traditional product design process. The advanced ecosystem accelerates new design workflows and improves how teams collaborate. This in turn, reduces the time taken for the approval of a design.
- General Motors’ Dreamcatcher uses Machine Language (ML) for economic prototyping.
The future of car designs lie with Generative Design, where AI algorithms generate hundreds of potential designs just by defining the product idea or the problem. Example — Volkswagen uses generative design to inspire compactness in its vehicles.
Use Case 2 — Manufacturing:
Car manufacturers are using AI in every facet of the car-making process. AI-based systems are enabling robots to pick parts from the conveyor belt with a high rate of success. Using deep learning, the robot automatically determines which parts to pick, how to pick, and in what sequence. This can significantly help reduce the number of workforces, and, in turn, boost the accuracy level of the process.
- Rethink’s robotics work with humans on the supply chain, tending machines, handling materials, performing tests, and packing finished products.
- Kia worked with Hyundai to develop wearable robots which aids in performing tedious tasks while learning the motion continuously.
If there is an unexpected machine failure on an automotive assembly line, the costs can be catastrophic. Hence, companies like KONUX feed the sensor data into an AI system that crunches it to improve system performance.
Future auto factories will only have flexible production stations supervised by unmanned systems like Audi’s vision 2035 smart factory.
Use Case 3 — Supply Chain:
Automotive supply chains are among the most complex networks in the world. An average vehicle has roughly 30,000 distinct parts, arriving from different suppliers across the globe. AI-powered supply chains are being used to analyze a massive amount of data to be able to forecast accurately.
Blue Yonder uses AI techniques to optimize its forecasting and replenishment while simultaneously adjusting pricing.
AI is allowing the fully automated self-control systems to make supply-chain management decisions, adjusting routes and volumes to meet the predicted demand spikes.
Use Case 4 — Quality Control:
Quality control, such as inspecting painted car bodies, is slow, tedious and prone to errors. AI-based machines can detect defects more accurately than humans.
Audi uses Machine Learning (ML) to recognize and mark the minutest cracks in sheet metal parts.
In the future, quality inspection using ML will replace the current optical crack detection.
The data drawn will be used to analyze the root causes of defects and improve overall production processes.
Use Case 5 — Car Dealership Experience:
AI is helping dealers become more efficient and transparent and deliver an enhanced customer experience.
The advanced data techniques are impacting the way consumers gather information about cars, learn about which car to purchase, and decide when to interact with a dealer.
- As most of the shoppers go online to read up on their cars, Keating Auto Group enhanced its partners with AI and ML technology solutions. The group receives personal data on active shoppers and their preferences on different models.
- Toyota also grew sales by using AI/ML to engage shoppers according to their preferences, serving up only those makes, models, trims, and offers that match each shopper’s wish list. They created personalized customer journeys through digital engagement, and the dealership was able to increase sales by 150 per cent.
Using AI technology, car dealers are trying to evolve from mere transaction hubs to experience hubs.
Use Case 6 — Automotive Insurance:
The automotive insurance industry is also on the verge of a tech-driven shift. The applications of AI in insurance are speeding up the process of filing claims when accidents occur.
- Ant Financial created software that analyzes vehicle damage and handles claims using machine vision.
- Ping a Property and Casualty Insurance Company, launched a nationwide credit-based smart auto insurance claims solution, which enables drivers to make claims through their smartphone by uploading photos for assessment by AI.
Along with the personalized experience, auto insurers also need to work on cyber theft. The more connected the car, driver, and passenger are to each other, the greater is the risk of cyber-crimes.
Use Case 7 — Driver Experience:
The Advanced Driver Assistance Systems (ADAS) not only helps with car parking, auto door lock, hands-free phone calls but also gathers insights about the vehicle, driver, driving habits and the passenger. Based on these insights, ADAS makes an informed decision.
- Ford, Honda, Mazda, and Benz are developing with Driver Attention Alert, which will recognize driving behaviour through steering input and directions, right from the start of the ride and compares the learned data during later stages of the ride.
- Nauto creates AI sensor technology for its commercial fleets. The technology reduces distracted driving, which further decreases the instances leading to collisions by assessing driver behaviours.
- Waymo’s 360-degree perception technology detects pedestrians, vehicles, cyclists, roadblocks, and other obstacles from a distance of about 300 yards.
As of now, ADAS studies the car and the driver. It monitors and evaluates critical parameters. Since ADAS is yet to boom to its full potential, exciting times ahead for the automotive industry.
Use Case 8 — Passenger Experience:
Consumers of today lack patience. They want everything on the go, including a response from their cars. Taking this psychology of modern consumers into account, car manufacturers are developing strategies and differentiated applications to upgrade the user experience.
- Ford introduced FordPass, a subscription-based dongle that plugs straight into the vehicle’s onboard diagnostics port. Also, Ford in collaboration with Amazon is offering In-car Delivery where the Amazon packages are securely received to your vehicle.
- Hyundai is developing a new in-car infotainment system that includes a personalized audio search experience and playlists, which is accessible to customers via voice commands.
Look at the state of the modern In-Vehicle Infotainment system video.
Why are Autonomous Cars still not in the Mainstream?
Technology has helped OEMs to disrupt the standards and build innovative possibilities. However, the autonomous cars which have been taking the spotlight for quite a few years, have still not hit the roads. Below are the main challenges working against the stream.
Security Vulnerabilities
- 75% of mobile applications fail basic security tests. As the number of sensors and connected smartphones increase rapidly in vehicles, there is a potential for hackers to steal Personally Identifiable Information (PII) and financial information through the connected devices.
- Cybercriminals can exploit flaws in a vendor’s implementation and can take control of the vehicle’s operation like the cruise control system to manipulate the steering and braking systems.
- With security testing taking place at the end of the production cycle, there is a lack of secured design systems.
360 Weather Test
- An autonomous vehicle is being tested in a safe and closed environment. This makes it unreliable to carry passengers safely in all weather conditions like a blizzard.
Regulatory Environment
- To create uniform standards for AV, autonomous vehicle legislation H.R. 3388 was passed in 2018. However, there are widespread fears when fleets of self-driving cars will be deployed on public roads to test safety. Hence the legislation remains fragmented.
Afterthoughts
Last decade saw innovations in myriads of industries, with AI being the fundamental element of the innovation. The automotive industry has been the driving force for AI, and it is obviously not only confined to autonomous driving.
OEMs are undergoing significant technological changes over the next decade. Primarily, the current market growth is driven by digital service offerings and are greatly endangered by the new mobile connectivity. However, OEMs must ensure data security and privacy while handling customer data.
In this competitive world, the earlier the businesses get on the AI journey and work on their data monetization strategies plus privacy, the quicker they will get a glimpse of ROI and will be able to stay ahead of the automotive curve in the future.
This post was written by Asokan Ashok, the CEO of UnfoldLabs. Ashok is an expert in driving customer insights into thriving businesses and commercializing products for scale. As a leading strategist in the technology industry, he is great at recommending strategies to address technology & market trends. Highly analytical and an industry visionary, Ashok is a sought after global high-tech industry thought leader and trusted strategic advisor by companies.
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For any comments or discussions, please feel free to reach out to Ashok or UnfoldLabs at “marketing-at-unfoldlabs-dot-com”.
