Page 57 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

In this table, we aim to collect the characteristics of each use case in different domains and
summarize them based on each factor. While studying these use cases, the different actors
(vehicles, sensors, roadside units, networks, controllers) and characteristics (regulations,
infrastructure, technology, interoperability, human factors, data types, data handling) are
listed to find common patterns, metrics, and evaluation mechanisms for the integration of AI
in different domains. The goal of this multi-domain study is to develop a framework assessing
AI readiness to indicate the ability to reap the benefits of AI integration. Efforts could then be
extended to scale this research across different regions of the world and other domains and
use cases.

Standard frameworks may (a) offer clear metrics for measuring readiness levels in terms of
enabling factors derived from the case-based analysis, (b) empower organizations, regions,
and countries to evaluate their preparedness to benefit from AI effectively, with respect to the
characteristics identified in the case-based analysis (c) study the various risk factors in simulated
and experimentation scenarios so as to make informed decisions and (d) apply regional and
domain-specific preferences while deploying AI-based solutions.

So, a methodological bottom-up approach is employed to study various use case scenarios
and to study the corresponding impacts of artificial intelligence.

Two main parts of our analysis are actors and characteristics related to the use case. Some
examples of actors are vehicles and humans, networks, controllers, and traffic management
systems. Actors may be equipped with or utilize sensor technologies that enable AI integration.
AI integration may require network connection with specific requirements such as low latency,
high throughput, and edge intelligence capabilities. Controllers, such as the AI/ML feedback
loop and pre-established traffic plans for specific scenarios, are integral in leveraging existing
infrastructures for AI implementation. Lastly, traffic management systems, encompassing
Roadside Units (RSUs), smart traffic signals, and sensor-equipped cameras, act as primary data
providers in the AI-integrated system.

Some of the characteristics studied are regulations and standards, data types, infrastructure,
algorithms and technology, interoperability, human factors, data handling, environmental
factors, and developers and open-source initiatives.

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