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        <description>SafeAV - The Handbook

Current version of PDF / Generate new PDF!

Todo list

Responsibilities

General rules

[NEW!] SafeAV specific rules

Table of Contents:

	*  Authors

	*  Introduction

	*  Autonomous Systems SUT-&gt;TalTech
		*  Definitions, Classification, and Levels of Autonomy [Draft]
		*  Legal, Ethical, and Regulatory Frameworks [Draft] 
		*  Introduction to Validation and Verification in Autonomy [Draft]
		*  Validation Requirements across Domains [Draft]
		*  cybersec 2026.05.18 Praha…</description>
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        <description>Guideline for the SafeAV book

NB! See Ch. 6 Control, Planning, and Decision-Making as an example

General Chapter Structure

Each chapter should follow a consistent structure and be linked to the book’s central V-model and verification and validation framework.

Every chapter will be 15–25 pages in PDF format.</description>
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        <description>SafeAV - Hands-on guide

Current version of PDF / Generate new PDF!

Rules for book development

Table of Contents:

	*  Authors
	*  Project Information

	*  Introduction

	*  Exercises

	*  Use cases
		*  Use Case #1 AV Shuttle TalTech
		*  Use Case #2 F1TENTH  CTU
		*  Use Case #3 Mobile Robot RTU
		*  Use Case #4 Drone SUT</description>
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        <title>Appendixes - [Indutsrial Use Case #2] </title>
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        <description>Appendixes

Tables

Autonomous Systems
 Domain  Primary Standards Body  Key Autonomy Standard  Ground  SAE  SAE J3016  Ground  ISO  ISO 26262, ISO 21448  Ground  UNECE  UN R157  Airborne  RTCA  DO-178C, DO-365  Airborne  FAA/EASA  UAV autonomy certification</description>
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Scenario design tells us what should be tested. Test methods determine how those scenarios are executed, what kind of evidence is collected, and how the results are translated into a validation argument. For planning and control, this distinction matters because the same concrete scenario can be exercised in several different ways: first in simulation, then in software-in-the-loop or hardware-in-the-loop settings, then on a controlled test track, and finally in a monitored real-wor…</description>
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        <description>Methods and Architectures

Having located planning and control within the autonomy stack, the next step is to examine the principal methods used to implement this layer. These methods fall broadly into classical control, AI-based control, behavioral decision-making, and motion-planning architectures, each with different implications for validation, traceability, and safety assurance.</description>
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        <description>Planning and Control in the AV Stack

Planning and control are the parts of an autonomous system where perception turns into action. A vehicle does not become autonomous simply by detecting objects or building a map of the world. It must also decide what to do next, determine how that decision can be executed safely, and then convert the planned motion into actuator commands. In other words, this chapter sits in the middle of the autonomy loop: it connects the understanding of the environment to…</description>
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