Artificial Intelligence In Transportation

How artificial intelligence in transportation can improve your everyday lives, Use cases of AI in transportation etc.

What you'll learn
How artificial intelligence in transportation can improve your everyday lives
Top use case of artificial intelligence in transportation
Implementation of artificial intelligence in transportation
Benefits of artificial intelligence in transportation industry
Real -world cases of artificial intelligence in logistics and transportation industry
Challenges and tips for adopting artificial intelligence in transportation

Requirements
Desire to learn more about artificial intelligence in transportation
No special requirement

Description
Artificial intelligence brings a host of advantages revolutionizing the industry and enhancing various aspects of the transportation ecosystem. Artificial intelligence, in tandem with other emerging technologies like internet of things, machine learning, cloud computing, big data analytics, and 5G, opens up a world of opportunities and new horizons for interconnectivity between vehicles paving the way for more efficient, advance, and safer transportation systems. The transportation industry is a sophisticated system with many external influencing factors, such as human errors and reactions, accidents, economic situations, and even time of year or day. Artificial intelligence uses all these data points to forecast the probability of different situations, thus, providing conditions for educated decisions. No wonder, international association of public transport reports that 86% of public transportation players are engaged in partnerships to develop and adapt artificial intelligence technology projects. Smart driver assistants such as self-parking, lane recognition, and adaptive cruise control have become commonplace for many new cars. Some of them, such as Hyundai's advanced cruise control, have been implemented widely. However, they do not do the drivers job completely. Although autonomous vehicle are already full self-driving is hard to implement and requires a lot of work. any confusion within real-time data transmission and processing in the vehicles can cause a fatal outcome. Hence, the projects are still not fully ready to run on every road.