Artificial Intelligence in Transportation Market, By Offering (Hardware and Software), By Machine Learning Technology (Deep Learning, Computer Vision, Context Awareness, Natural Language Processing), By Application (Autonomous Trucks, HMI in Trucks, Semi-Autonomous Trucks), By Geography (North America, Latin America, Europe, Asia Pacific, Middle East & Africa)

Published In : Jun 2024

Code : CMI7079

Pages :160

Formats :

Excel and PDF

Industry : Automotive and Transportation

Market Challenges: Lack of standardization

Lack of standardization is one of the major factors restraining the growth of global artificial intelligence in transportation market. When there are no common standards, every company develops AI systems based on their own methodology and approaches. This results in solutions that are not interoperable with each other. For example, there are different types of neural networks and machine learning algorithms used by major players like Tesla, Uber and Waymo for developing self-driving car technologies. However, their solutions cannot communicate with each other due to absence of uniform technology standards. This restricts collaboration between companies and slows down innovation.

Moreover, without common standards, it also becomes difficult to ensure safety, reliability and security of AI systems used in transportation sector. Every company has its own way of addressing issues like bias, transparency and accountability in their algorithms. But lack of industry-wide protocols on auditing AI decisions and identifying faults increases the risk of anomalies.

Market Opportunities: Integration of AI with IoT

The integration of AI and IoT holds immense potential to transform the global transportation market. As autonomous vehicles start hitting the roads powered by computer vision, deep learning and other AI technologies, connected infrastructure deployed with IoT sensors will be crucial for safe navigation and efficiency. Real-time data sharing between vehicles and infrastructure facilitated by IoT networks can help optimize traffic flows, predict congestion points and reroute vehicles accordingly. This will lead to higher throughput and better utilization of road networks.

AI and IoT also offer opportunities to improve public transport systems. Integrating IoT sensors in buses and trains with predictive analytics tools can help transport authorities make smarter timetabling decisions based on ridership projections. This ensures minimal waiting times. Live traffic updates sent to mobile applications can further help commuters choose the fastest routes using different modes of transportation. Transition to autonomous buses and trains is also expected to improve accessibility for the elderly and differently-abled by providing door-to-door services on-demand.