The automotive technology market will continue to evolve as cognitive computing enhances the transportation sector. Self-learning artificial intelligence (AI) in automobiles is the key to unlocking the capabilities of autonomous cars and enhancing value to end users through virtual assistance.
This nascent technology offers original equipment manufacturers (OEMs) access to new revenue streams through licensing, partnerships and mobility services. Simultaneously, the use-case scenarios of self-learning AI in cars are drawing several IT vendors, Internet of Things (IoT) technology companies and mobile network service providers to the automotive industry.
The combined technologies have also attracted attention and investments from governments, due to its potential to improve urban lifestyles and add national and local economic development value.
Automotive AI Market Development
Frost & Sullivan -- which already offers market insights into power-trains, car-sharing and smart mobility management -- has recently released their latest analyses of artificial intelligence apps within vehicles.
By 2025, four levels of self-learning technology will disrupt the automotive industry, according to their latest market assessment. Level 4 self-learning car ownership will stimulate market growth, and create new partnerships with original equipment manufacturers (OEMs).
In fact, OEMs are making strategic investments or acquisitions for Level 3 and Level 4 self-learning technology -- Frost & Sullivan believes that there are already several prominent startups in the market.
"Technology companies are expected to be the new Tier I for OEMs for deep-learning technology," said Sistla Raghuvamsi, research analyst at Frost & Sullivan. "Google and NVIDIA will be key companies within this space, dominating the market by 2025."
Meanwhile, thirteen OEMs will be investing over $7 billion in the development of various AI technology use cases. In particular, Hyundai, Toyota, and GM will account for 53.4 percent of the total investment share.
Outlook for Automotive AI Advancement
The key challenge for technology developers is gathering the data required to train AI to support self-driving capabilities. That need is advancing artificial simulation research to test trained AI, as well as create low-cost level 2 systems for driver analytics and assistance that can eventually provide data for levels 3 and 4.
"High processing capability with low power consumption will be critical to enable various levels of self-learning cars," noted Raghuvamsi. "By 2025, level 4 self-learning cars will integrate home, work and commercial networks, enhancing the value to end users."