The list of book contributors is presented below.

• Raivo Sell, Ph. D., ING-PAED IGIP

• Roman Czyba, Ph. D., DSc., Eng.
• Piotr Czekalski, Ph. D., Eng.
• Tomasz Grzejszczak, Ph. D., Eng.

• Agris Nikitenko, Ph. D., Eng.
• Karlis Berkolds, M. sc., Eng.

• Tomasz Siwy, CEO

• Ing. Libor Přeučil, CSc.

This content was implemented under the following project:

• Cooperation Partnerships in higher education, 2024, SafeAV: Harmonizations of Autonomous Vehicle Safety Validation and Verification for Higher Education: 2024-1-EE01-KA220-HED-000245441.

Consortium Partners

• Silesian University of Technology, Gliwice, Poland (Coordinator),
• Riga Technical University, Riga, Latvia,
• Czech Technical University in Prague, Prague, Czech Republic
• Tallinn University of Technology, Tallinn, Estonia
• ITT Group, Tallinn, Estonia.
• Prodron, Gliwice, Poland

Erasmus+ Disclaimer
This project has been funded with support from the European Commission.
This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Copyright Notice
This content was created by the SafeAV Consortium 2024–2027.
The content is copyrighted and distributed under CC BY-NC Creative Commons Licence and is free for non-commercial use.

In case of commercial use, please get in touch with MultiASM Consortium representative.

[raivo]Please fill in some introduction

The book comprises a comprehensive guide for a variety of education levels. A brief classification of the contents regarding target groups may help in a selective reading of the book and ease finding the correct chapters for the desired education level. To inform a reader about the proposed target group, icons are assigned to the top headers of the chapters. The list of icons and their reflection on the target groups is presented in the table 1.

Table 1: List of icons presenting content classification and corresponding target groups

Icon	Target group
	Bachelor and Engineering level students
	Masters students

put your contents here

[rczyba]

[rczyba]

[rczyba]

[rczyba]

[rczyba]

[raivo.sell]

[raivo.sell]

[raivo.sell]

[raivo.sell]

[pczekalski]

put your contents here

[karlisberkolds]

[karlisberkolds]

[karlisberkolds]

[raivo.sell]

[raivo.sell]

[raivo.sell]

put your contents here

[karlisberkolds]

[karlisberkolds]

[karlisberkolds]

[raivo.sell]

[raivo.sell]

[raivo.sell]

put your contents here

[preucil]

[preucil]

[preucil]

[preucil]

[preucil]

put your contents here

[raivo.sell]

[rczyba]

[rczyba]

[raivo.sell]

[raivo.sell]

put your contents here

[raivo.sell]

[raivo.sell]

This chapter explores the specificities of Human-Machine Interaction (HMI) in the context of autonomous vehicles. It examines how HMI in autonomous vehicles differs fundamentally from traditional car dashboards. With the human driver no longer actively involved in operating the vehicle, the challenge arises: how should AI-driven systems communicate effectively with passengers, pedestrians, and other road users?

This section addresses the available communication channels and discusses how these channels must be redefined and implemented to accommodate the new paradigm. Additionally, it considers how various environmental factors—including cultural, geographical, seasonal, and spatial elements—can impact communication strategies.

A concept, the Language of Driving (LoD), will be introduced, offering a framework for structuring and standardizing communication in autonomous vehicle contexts.

Understanding how humans perceive the world is crucial for autonomous vehicles to effectively communicate and interact with them. This chapter explores how human perception, driven by sensory input and cognitive processing, can inform the development of autonomous perception systems, emphasizing the parallels between human and animal intelligence in recognizing focus, body positioning, gestures, and movement. By examining innate perceptual capabilities such as basic physics calculations and environmental modeling, AVs can better anticipate human behavior and respond appropriately in complex traffic environments.

This chapter explores how AVs might adopt human-like communication methods, such as facial expressions or humanoid interfaces, to effectively interact in complex social driving environments.

Human communities build languages for cooperative teaming. To participate in the act of cooperative transportation, AVs will have to understand this language. Depending on the level of expectation communicated by the AV, this language may extend into social interaction models.

A key requirement of an effective Passenger Communication system is to have in-built fail-safe mechanisms based on the environment. AVSC has worked with SAE ITC to build group standards around the safe deployment of SAE Level 4 and Level 5 ADS and has recently released an “AVSC Best Practice for Passenger-Initiated Emergency Trip Interruption.”However, passenger communication extends beyond emergency stop and call functions. Warnings and explanations of unexpected maneuvers may need to be communicated to passengers even when there is no immediate danger. This should replicate and replace the function that a human bus driver would typically perform in such situations.

Communication between the car and pedestrians at a crosswalk is a difficult and important problem for automation.

The role of conventional and LLM based AI in HMI.

[raivo.sell]

[raivo.sell]

[raivo.sell]

[raivo.sell]

put your contents here

[raivo.sell]

[raivo.sell]

[raivo.sell]

[raivo.sell]