If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
From the first mass produced cars to passenger aircraft breaking the sound barrier, there have been numerous advances within the area of transportation that have had a profound effect on the way in which we approach travel and transport. However, the latest technological advance to begin to revolutionize transportation may come to dwarf any and all that arrived before it. And its uses are many. In this article, we'll being looking at a few examples of artificial intelligence within transportation and how it is helping to meet several of the most common and persistent challenges in this area. There are several challenges that are persistent throughout the transportation industry and that have plagued this sector ever since its inception.
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.
Interest in emergent communication has recently surged in Machine Learning. The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve problems that already have a viable solution. Here, we consider self-driving cars coordinating with each other and focus on how communication influences the agents' collective behavior. Our main result is that communication helps (most) with adverse conditions.
The artificial intelligence in transportation market is projected to grow at a CAGR of 17.87% from 2017 to 2030, and the market size is expected to grow from USD 1.21 Billion in 2017 to USD 10.30 Billion by 2030. The increasing government regulations for vehicle safety, growing adoption of advanced driver assistance systems (ADAS), and development of autonomous vehicles play a significant role in the growth of this market.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.
David Poulsen, CutCableToday's IT expert, says connected, or autonomous, vehicles, are attractive because of the technologies that undergird them. "The Internet of Things (IoT) is one part of the equation," Poulsen explains. "The other part is artificial intelligence (AI). It acts as the driver, helping the connected'thing,' which could be a vehicle or inventory system, make smarter decisions."
When Greg Rogers left his gig as a Washington, DC, lobbyist in 2015, he did what any savvy, mid-20s kid with a car and a light wallet might: He signed up to drive for a couple of ridehailing services. "Living the millennial dream means quitting your job, driving for Uber and Lyft, and trying to figure it out," he says.
Smart Internet of Things (IoT) in transit service has public transits connected to the networks accessible via internet. The networks connect to each other also the external environment to share data picked up by sensors on the transits themselves, offering many benefits to improve public transportation. Supported by the smart IoT connectivity integrated with Artificial Intelligence (AI), various on-board systems for transit vehicles have emerged, making the transit system more reliable, convenient and efficient for passengers. Multiple plane, train, metro, and bus companies have started using smart IoT in their services to enhance the customer experience and to help operations and maintenance. One good example is the Utah Transit Authority (UTA), which has provided a smart connected public busing service across a 1,600-square mile service region serving 80 percent of Utah's entire population.