A driverless car running on the road is like a screenshot from a sci-fi movie. However, fiction is becoming a reality, and thanks to #Artificial Intelligence (AI). AI technology complements the concept of self-driving cars.
Elon Musk had in 2017 that all cars will be #autonomous in 10 years without any steering wheel. We are very close to bringing this estimate to reality in just 4 years.
Mercedes-Benz, Volvo, Bosch, Nissan, General Motors, and Continental Automotive Systems, along with a few other automotive players, are trying to take advantage of First-Mover in the market through the development of autonomous cars, using AI.
Advances in Artificial Intelligence have had an enormous contribution to the growth of the automotive industry. By 2025, the annual value of AI in the automotive industry will reach $ 215 billion and it will become a mainstream trend from here. The United States is currently the largest automobile market worldwide, in terms of production and sales.
The installation of AI-based systems will increase by 109% in 2025. Major players such as Audi, Tesla, Hyundai, Benz, Nissan, Kia are trying desperately to integrate AI, automated cars, and develop tools that help. Installation of self-sufficient cars.
AI & Automotive — Use cases
The following is the wave of disruptions caused by AI in the automotive industry at high levels of abstraction.
Use Case 1 — Design:
From brainstorming to developing a working car model, design and development engineers consider many aspects to be as user-friendly as possible.
Here are some use cases that show how OEMs use AI to accelerate their design
workflows:
Nvidia’s architecture uses AI, real-time ray tracing and programmable shading to transform the traditional product design process. The sophisticated ecosystem accelerates new design workflows and improves how teams collaborate. This reduces the time taken for design approval.
General Motors DreamCatcher uses Machine Language (ML) for economic modeling.
To Know About- Predictions On Self Driving Car Timeline From Top Automakers In The World
The future of car designs lies with generative design, where AI algorithms produce hundreds of potential designs by defining a product idea or problem. Example — Volkswagen uses manufacturing design to inspire compactness in its vehicles.
Use Case 2 — Preparation:
Car manufacturers are using AI in the car manufacturing process. AI-based systems enable robots to pick up components from the conveyor belt at a higher rate. Using deep learning, the robot automatically decides which parts to choose, how to select them, and in what order. This helps significantly in reducing the workforce number and, in turn, increases the accuracy of the process.
If there is a non-machine failure in the automotive assembly line, the costs can be catastrophic. Therefore, companies like Konex feed the sensor data into an AI system that crunches to improve system performance.
Future auto factories will only have flexible production centers under the supervision of unmanned systems such as the Audi Vision 2035 Smart Factory.
Use Case 3 — Supply Chain:
The automotive supply chains are one of the most complex networks in the world. There are about 30,000 different parts in the average vehicle, coming from various suppliers worldwide. AI-powered supply chains are being used to analyze massive amounts of data that can be accurately predicted.
Blue Yonder uses AI techniques to optimize its estimation and replenishment while adjusting prices.
To Know About-AI In Transportation: Artificial Intelligence in the Transportation Industry
AI allows fully automated automation systems to make supply-chain management decisions, adjust routes and volumes to meet projected spikes.
Use Case 4 — Quality Control:
Quality control, such as inspecting painted car bodies, is slow, laborious, and vulnerable. AI-based machines can detect defects more accurately than humans.
Audi uses #Machine Learning (ML) to detect and identify the smallest cracks in sheet metal parts.
In the future, quality checking using ML will replace current optical crack detection.
The data drawn is used to analyze the root causes of errors and improve overall production processes.
Use Case 5 — User Experience:
Today’s consumers have no patience. They want everything on the go, including a response from their cars. Considering this mentality of the modern consumer, car manufacturers are developing strategies and different applications to upgrade the user experience.
Ford has introduced a subscription-based dongle that plugs directly into the vehicle’s onboard diagnostics port. Also, in collaboration with Amazon, Ford offers in-car delivery, where Amazon packages securely receive your vehicle.
Hyundai is developing a new in-car infotainment system that includes a personalized audio search experience and playlists, available to users via voice commands.
Why aren’t autonomous cars still in the mainstream?
Technology has helped OEMs meet standards and create innovative opportunities. However, autonomous cars that have been in the limelight for some years have not yet hit the road. Below are the main challenges of working against the stream.
Security vulnerability:
75% of mobile apps fail basic security tests. As the number of sensors and connected smartphones in vehicles grows rapidly, hackers have the potential to steal personally identifiable information (PII) and financial information through connected devices.
#Cybercriminals can exploit the seller’s imperfections and control the vehicle’s operation, such as a cruise control system to change steering and braking systems.
Control environment:
To create uniform standards for the AV, the Autonomous Vehicle Act was adopted by H.R. 3388 was approved in 2018. However, there are widespread fears when deploying self-driving car fleets on public roads to test safety. And so the law was broken.
Final Thoughts:
Innovations have emerged in many industries over the past decade, with AI being a fundamental aspect of innovation. The automotive industry is the driving force for AI and is not limited to #autonomous driving.
WRITTEN BY
