Table of Contents

Authors

The list of book contributors is presented below.

Tallinn University of Technology
Silesian University of Technology
Riga Technical University
ProDron
Czech Technical University
External Contributors
Technical editing
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Introduction

[raivo]Please fill in some introduction

Content classification hints

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
 Bachelors (1st level) classification icon Bachelor and Engineering level students
 Masters (2nd level) classification icon Masters students

Autonomous Vehicles

put your contents here

[rczyba]

Autonomous Systems

 Bachelors (1st level) classification icon

[rczyba]

Ground, Aerial, and Marine Vehicle Architectures

 Bachelors (1st level) classification icon

[rczyba]

Domain-Specific Challenges in Autonomy

 Bachelors (1st level) classification icon

[rczyba]

Definitions, Classification, and Levels of Autonomy

 Bachelors (1st level) classification icon

[rczyba]

 Bachelors (1st level) classification icon

[raivo.sell]

Introduction to Validation and Verification in Autonomy

 Bachelors (1st level) classification icon

[raivo.sell]

Validation Requirements across Domains

 Bachelors (1st level) classification icon

[raivo.sell]

Intersection of Autonomy with Governance

 Masters (2nd level) classification icon

[raivo.sell]

Cybersecurity

 Masters (2nd level) classification icon

[pczekalski]

Hardware and Sensing Technologies

put your contents here

[karlisberkolds]

Sensors, Computing Units, and Navigation Systems

 Bachelors (1st level) classification icon

[karlisberkolds]

Hardware Integration and Supply Chain Considerations

 Bachelors (1st level) classification icon

[karlisberkolds]

Validating Sensors

 Bachelors (1st level) classification icon

[raivo.sell]

Governance, EMC

 Bachelors (1st level) classification icon

[raivo.sell]

Calibration, Maintenance, and Supply Chain

 Masters (2nd level) classification icon

[raivo.sell]

Software Systems and Middleware

put your contents here

[karlisberkolds]

Autonomy Software Stacks

 Bachelors (1st level) classification icon

[karlisberkolds]

Software Lifecycle and Configuration Management

 Bachelors (1st level) classification icon

[karlisberkolds]

Testing Software Systems

 Bachelors (1st level) classification icon

[raivo.sell]

Governance Safety Critical systems

 Bachelors (1st level) classification icon

[raivo.sell]

Open issues of validating AI components

 Masters (2nd level) classification icon

[raivo.sell]

Perception, Mapping and Localisation

put your contents here

[preucil]

Object Detection, Sensor Fusion, Mapping, and Positioning

 Bachelors (1st level) classification icon

[preucil]

AI-based Perception and Scene Understanding

 Bachelors (1st level) classification icon

[preucil]

Sources of Instability

 Bachelors (1st level) classification icon

[preucil]

Validation Approaches

 Bachelors (1st level) classification icon

[preucil]

Control, Planning, and Decision-Making

put your contents here

[raivo.sell]

Classical and AI-Based Control Strategies

 Bachelors (1st level) classification icon

[rczyba]

Motion Planning and Behavioural Algorithms

 Bachelors (1st level) classification icon

[rczyba]

Validation of Control & Planning

 Bachelors (1st level) classification icon

[raivo.sell]

Simulation & Formal Methods

 Bachelors (1st level) classification icon

[raivo.sell]

Human-Machine Communication

put your contents here

[raivo.sell]

Human Machine Interface and Communication

 Bachelors (1st level) classification icon

[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.

Human Perception and Driving

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.

Cultural and Social Interactions

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.

Language of Driving

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.

Passenger Communication

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.

Pedestrian Communication

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

AI role on communication

The role of conventional and LLM based AI in HMI.

Modes of Interactions

 Bachelors (1st level) classification icon

[raivo.sell]

Language of Driving Concepts

 Bachelors (1st level) classification icon

[raivo.sell]

Safety Concerns and Public Acceptance

 Bachelors (1st level) classification icon

[raivo.sell]

Verification & Validation of HMI

 Masters (2nd level) classification icon

[raivo.sell]

Autonomy Validation Tools

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Overview of V&V Techniques

 Bachelors (1st level) classification icon

[raivo.sell]

Testing Infrastructure

 Masters (2nd level) classification icon

[raivo.sell]

Challenges Ahead

 Masters (2nd level) classification icon

[raivo.sell]

Research Outlook

 Masters (2nd level) classification icon

[raivo.sell]