AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control

TL;DR

This paper explores how AI technologies can enhance traffic management and vehicle automation in Smart Cities by analyzing perception, control, and modeling, while addressing challenges like data integration, trust, and explainability.

Contribution

It provides an overview of AI-based traffic technologies and discusses open research challenges and standardization efforts for vehicle automation in Smart Cities.

Findings

AI enables improved traffic flow and vehicle automation.

Trust and explainability remain key challenges for AI in traffic systems.

Standardization is crucial for integrating AI solutions in Smart Cities.

Abstract

Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusion it is difficult for humans to trust it.