Artificial Intelligence (AI) will revolutionize the automotive industry in the next 20 years. Autonomous vehicles will become common, and AI will also improve the auto-manufacturing process, from design to management. These changes are imminent. Manufacturers need to identify the key sources of value and develop the skills and culture to leverage AI.
AI in the Automotive Industry: A Transformative Force
AI services have various applications in the automotive sector, such as advanced driver assistance systems that sense and react to road hazards and voice recognition technology that lets drivers communicate hands-free. AI services can also enhance the automotive sector’s efficiency, safety, and environmental performance by improving fuel consumption and enabling self-driving cars. The automotive industry will see more AI innovations in the near future as artificial intelligence keeps advancing and transforming the sector.
AI in the Automotive Value Chain
With AI applied across all stages of the value chain, the automotive industry stands out from other sectors using the technology:
Manufacturing
AI is a key factor in the innovation and optimization of the modern car industry. It enables smarter car design, more efficient car assembly with robots and exoskeletons, and better supply chain management.
Transportation
AI plays a vital role in driver assistance technology. It enables drivers to navigate highways with ease, recognizes their fatigue levels, and detects critical defects in a vehicle. AI reduces the stress and increases the safety of driving.
Service
AI systems can enhance the performance and efficiency of vehicles by using IoT sensors to monitor engine health and battery usage. AI can also offer better insurance solutions, such as faster and fairer claims processing.
Use Cases of AI in the Automotive Industry
Artificial Intelligence (AI) has transformed the automotive industry in numerous ways, enhancing safety, efficiency, and the overall driving experience. Here are several key use cases of AI in the automotive sector, including the mentioned driver assistance:
AI in Automotive Manufacturing
AI in automotive is transforming the industry by enabling manufacturers to improve the quality and efficiency of their products and processes. AI systems can help detect and prevent defects, optimize workflows, reduce waste, and assist with design and innovation. For example, Audi uses computer vision to spot tiny cracks in sheet metal quickly and accurately.
Some of the common applications of AI in automotive are:
- Computer vision for malfunction detection
- Enhanced quality control and decreased waste management process
- Predictive maintenance to improve the efficiency of production equipment
- Assistance with the creation of the products at the design stage and discovering algorithms to optimize the use of power units and build more efficient models
Additionally, AI-powered robots can work alongside human workers to move and assemble parts using SLAM technology. This can increase productivity and efficiency by up to 20%.
AI in automotive is a powerful tool that can help manufacturers produce better and safer products while saving time and money.
How AI in Automotive Works
5 levels of vehicle automation
One of the most widespread AI applications in the automotive industry is advanced driver-assistance systems (ADAS). According to the World Health Organization, Human error is a major factor in many traffic accidents, which cause 1.35 million deaths every year worldwide. Besides improving road infrastructures and traffic laws, machine learning development is a promising way to reduce human error and enhance road safety.
The National Highway Traffic Safety Administration (NHTSA) defines six levels of driving autonomy, as shown in the image below. Most AI-powered vehicles today are between levels 1 and 3 on the NHTSA scale. To achieve full autonomy, where a driver can become a passenger and let the machine drive, a car needs a lot of ML model training.
To achieve full autonomy, a vehicle must handle more than 1 TB of data per second on average. This data is sent to cloud platforms, where it is annotated, processed, and used to improve these algorithms. However, this approach is not compatible with the current demand for low energy consumption and has some economic challenges for the widespread adoption of autonomous vehicles.
Driverless vehicles have shown some success, but they mostly learned one route only. This lets an AI system create a very detailed map of the road, but it is not useful for larger-scale autonomous vehicles, because it is impossible to map every route in one area.
AI algorithms
There is a combination of three types of AI algorithms that are most commonly used in autonomous vehicles: regression, cluster, and decision matrix:
In autonomous driving, regression algorithms predict the outcome of various situations based on historical data. For instance, regression algorithms can forecast how likely it is that a pedestrian will cross the vehicle's way or that an obstacle will appear on a certain road. Furthermore, these algorithms collect and process data during each trip, which helps them to improve their decision-making skills over time.
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Cluster algorithms are essential for autonomous vehicles to process and make sense of the huge data streams from various sources such as sensors, cameras, and GPS systems. These algorithms enable the vehicle to recognize and classify objects on the road and react accordingly, for example by braking or switching lanes. Cluster algorithms also assist in route planning and navigation, continuously adjusting and improving the route based on real-time traffic situations.
Decision matrix algorithms are useful for making choices. They use predefined rules and outcomes that are coded into the algorithm, but they can also consider real-time data from a vehicle's sensors. These algorithms decide the car’s next action, such as taking a turn and which direction to go. The success of these actions depends on the algorithm's ability to correctly identify, recognize, and anticipate an object's next movement.
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We hope this edition has provided valuable insights into how AI is transforming the automotive industry. AI is not only a powerful tool for innovation, but also a strategic asset for competitiveness and sustainability. As the automotive industry embraces the software revolution, it will face new opportunities and challenges that will require collaboration, adaptation, and regulation.
We look forward to sharing more updates and insights with you in our next edition. Thank you for reading.
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