Artificial intelligence (AI) continuously diffusing into more and more technological applications, including telematics and fleet management. AI provides opportunities to increase driver safety while reducing overall costs and increasing efficiency. AI-empowered IoT, IoV, and Data Analytics could help to reduce the number of critical errors and result in better decisions from both human and machine sides. Here we discuss how AI-based solutions could advance telematics and fleet management.

What is AI

AI as a term is not really new since it originates from around 1955. AI in a broad sense is an artificial intelligence program capable of tackling every kind of task it’s presented. It sometimes referred to as General AI. The actual general AI case is still pretty far from final realizations, and some researchers arguing that we’ll never get there. Term Narrow AI is more like a simplified version of general AI and defines an artificial intelligence program, capable of solving a single, broad, or specific but well-defined task.

AI as a subject rooted in a broad range of disciplines ranging from Computer Science and Mathematics to Biology and Engineering. AI general aim is the development of computer functions associated with human intelligence, including reasoning, learning, and problem-solving.

Increasing fleet efficiency with AI

In comparison to “traditional” programming, AI-enabled computing could answer generic questions, allows putting highly independent pieces of information together with further efficient modifications, provides easy and fast program modification. AI deploying is a step-by-step process, that takes into account pre-existing processes and generally includes following steps:

  • Characterizing a problem and business case.
  • Developing the methodology, targeting the problem.
  • Design a deployment pathway to integrate AI into existing workflows.
  • Design and implement a methodology for continuous evaluation.

Continuous evaluation insures that model will stay updated and will run correctly upon arrival of new fresh datasets.

AI reference architecture

Here we briefly go through AI reference architecture to understand what kind of infrastructure is needed to successfully implement AI and IoT applications.

The data integration layer basically accumulates data from various sources including enterprise information systems and IoT networks. Data Persistence layer aggregates and process various types of structured and unstructured data using a wide range of database technologies.

Increasing fleet efficiency with AI

Platform Services contain sophisticated platform services that are necessary for any enterprise AI or IoT application.  Analytics Processing on its turn performs continuous analytics processing, MapReduce, batch processing, as well as stream processing and recursive processing. Next is Machine Learning Service which general purpose is to enable data scientists and engineers to develop/deploy machine learning models. Further data visualization tools allow a rich and varied set of data visualization tools including Tableau, Excel, Spotfire, Oracle BI, and many others. Developer Tools and UI Frameworks ensure convenient application development frameworks and user interface (UI) development tools. An AI and IoT platform must support the vast majority of these tools in order to provide IT developers teams with powerful and convenient development instruments.

AI applications in Fleet Management

AI-empowered fleet management systems could collect a massive array of historical and real-time data for further use in predictive maintenance, insightful analytics, driver behavior analysis, and autonomous driving. These applications are only a few from a long list of possible use-cases.

Increasing fleet efficiency with AI

AI-based telematics solution would most probably include a modern telematics platform, a set of IoT and tracking devices as well as sensors. It rather complex but highly effective and dedicated solution. For instance, such a combination allows effective driver behavior analytics and throughout control while avoiding most common violations, including speeding, harsh driving, and excessive idling.

Increasing fleet efficiency with AI

A modern innovation-driven telematics platform, developed while keeping an eye on AI, IoT, and related modern technologies allow profound visibility with HD-tracking, rich set of tools and maps, and real-time telemetry data. It also helps in the fleet, cargo, and equipment protection from theft or unauthorized use.

Increasing fleet efficiency with AI

AI indeed has all the potential to further accelerate telematics and fleet management platforms functionality and with a time become a crucial component of such platforms.


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  • Jay Lee, Industrial AI, 2020.
  • A. Raman and W. H. Tok, A Developer’s Guide to Building Al Applications, 2018.