The automobile industry is looking toward autonomous vehicle systems as its future. The global automotive artificial intelligence market is forecasted to grow to US$ 7,676.92 million by 2028, with a CAGR of 31.30% in 2022-28. Whether it is passenger vehicles, commercial, logistics, or other transportation, major manufacturers are adopting artificial intelligence in one way or another.
In other word, the automobile industry utilizes machine learning technology to offer features like automated parking, reverse driving, temperature control, safety, and brake systems.
Usage of AI & ML in the Automobile Industry
Artificial intelligence and machine learning technology are utilized in coordination across the automotive value chain. Relying on the annotation of big data, they are playing a crucial role in the manufacturing, operation, and creation of next-generation vehicles. Based on the different verticals of the automobile industry, AI/ML usage can be classified as follows:
Big data-driven manufacturing strategies took the form of digital twin processes in designing automobiles. This designing process is based on video annotation services, where converted information is fed into the AI systems using machine language. One such design strategy is digital twins, which uses annotated design data to imitate the structure in manufacturing. This helps in achieving faster & cost-effective development of high-quality standards.
- Collaborative Robots
Operating simultaneously with humans, robots learn and execute automobile design, manufacturing, and assembly. Data annotation-based ML technology assists these robots in collaborating with human hands to produce vehicle units.
For instance, the major advantage of using AI/ML models in vehicle assembly lines has been detecting and preventing fatal mishaps in factories. They also help maintain quality by identifying defects and irregularities in material or components. The North American plant of Hyundai started using AI-powered wearable robots in 2018.
- Predictive Maintenance
Artificial intelligence and machine learning operate in tandem, shifting the whole vehicle service landscape from preventive to predictive maintenance.
The machine learning algorithms get smarter in identifying shortcomings in the parts with time, enabling AI systems to predict the possible wear & tear. This generates better results than human guesswork during vehicle service.
In conclusion, based on the image annotation data of spare parts and audio annotation of their sound, the machine language algorithms empower AI better than the human guess. Moreover, this forecasts the overhauling according to the friction in parts, breakage in rings, scratches, etc.
In conclusion, predictive vehicle maintenance has benefitted users by improving vehicle efficiency and availability and reducing depreciation.
- Service Claim Settlements
AI-powered insurance providers avail quick service options for clients. Though, text data annotation is used to analyze claims, and AI systems offer services accordingly in smoothening the whole settlement process.
- Analyzing Driving Conditions
AI-powered vehicles operate on images and video data of streets, traffic, and road conditions. .
- Facial Recognition
Image annotation data is fed into machine learning algorithms to train AI and create respective vehicle entry access.
- Autonomous Vehicles
Manufacturers like Tesla, Ford, etc., are working on the concept of fully autonomous vehicles. Though, partially autonomous vehicles are available, there might soon be vehicles with full autonomous control.
- Self-Driving Cars
Tesla’s autopilot is an example of this kind of AI and ML-based vehicles. It operates on 1.5 petabytes of data. This includes a million 10-second videos and 6 billion objects. Though this data’s annotation with depth, velocity and bounding boxes gives the user better results.
- Interconnected Vehicles
Interoperability and seamless connectivity, along with maintaining safety for the passengers. Therefor, this is the concept behind interconnected AI-based vehicles. However, they are aimed at providing better transportation facilities.
- Analyzing Driver Behavior
With AI in control, the mishaps happening due to driver’s mistakes can be reduced. For instance, the AI will keep a check on the driver’s eyes and warn when eyelid movements predict drowsiness.
- Driving Assistance
Right from managing your vehicle’s infotainment system with gestures to adjusting seats, controlling temperature, and adjusting mirrors, everything for driver’s assistance is provided by AI systems to give a pleasant ride.
- Pollution Control
AI and ML can contribute to carbon footprint reduction by controlling automotive emissions. They can help track, monitor, and control carbon emissions by taking necessary measures. Above all, AI systems can coordinate with satellite data on weather and pollution statistics, taking steps to channel the vehicle’s average emission.
The Future Ahead
In conclusion, with the emergence of new technologies in the data enrichment services, and a growing expert base in text, data, and video annotation services, the future of AI seems to be bright.
Though presently, the automobile industry is capable of analyzing only 12% of available data, the data annotation services can build collaboration to increase the output. Therefore, this will conclude to increase the pace of AI-based automobiles.