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Safeguarding Ai-Based Perception Functions For Highly Automated

Di: Ava

The development of automated work machine systems towards autonomous operation is proceeding rapidly in different industrial sectors. The aim of the study was to explore the current situation and possible differences in standardization supporting the development in different industrial sectors. The existing ISO and IEC standards and work items related to autonomous Request PDF | Towards a Scenario-Based Assessment Method for Highly Automated Driving Functions | Current research into highly automated driving (HAD) functions aims to support drivers in various

Safeguarding Generative AI With Cybersecurity Measures - PWOnlyIAS

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for

En Route to the Virtual Verification of Automated Driving Functions

Scenario based methods for testing and validation of automated driving systems (ADS) in virtual test environments are gaining importance and becoming an essential component for verification and validation processes of ADS. Autonomous driving is based in large part on artificial intelligence (AI), machine learning and neural networks. Because there is no possibility for human validation of the machine perception and the resulting decisions when using these technologies, other ways have to be found to analyze the accuracy of the machine perception.

In previous part of the Introduction to Self-Driving Car series, we discussed a core visual functionality of perception stack in an autonomous vehicle (AV): computer vision. In it we focused on For this purpose, a virtual test field is set up to address both technical and social issues regarding automated and connected mobility in urban areas. A particularly important aspect for the safeguarding of autonomous driving functions is the selection of relevant traffic scenarios and their detailed modelling.

There are three major areas of background work related to this paper: State of practice and related research on certification of highly automated railway sys-tems, state of practice in railway simulators, and state of the art in the automo-tive domain related to verification and validation of autonomous driving systems. Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions.

Mock MScholz SBlank FHüger FRohatschek ASchwarz LStauner T (2021)An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated DrivingComputer Safety, Reliability, and Security. Advancing Deepfake Detection Using Xception Architecture: A Robust Approach for Safeguarding against Fabricated News on Social Media The application of AI is a key enabler for highly automated driving. Initiated by VDA, a consortium of OEMs, suppliers, technology providers and scientific institutions is developing a methodology for a novel safety argumentation in the project „KI Absicherung“ (safe AI) that systematically identifies insufficiencies of AI-based functions, makes them measurable and

An autonomous vehicle must be able to perceive its environment and react adequately to it. In highly automated vehicles, camera-based perception is increas- ingly performed by artificial intelligence (AI). One of the greatest challenges in integrating these technologies in highly automated vehicles is to ensure the func- tional safety of the The significance of V2X (Vehicle-to-Everything) technology in the context of highly automated and autonomous vehicles can hardly be overestimated. While V2X is not considered a standalone Abstract This paper describes the challenges involved in arguing the safety of highly automated driving functions which make use of machine learning techniques. An assurance case structure is used to highlight the systems engineering and validation considerations when applying machine learning methods for highly automated driving.

In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Because of this, and because of the technologies which have been recently implemented in the field of driving functions (for example, methods based on Artificial Intelligence (AI) [2]), the available verification methods are reaching their limits. New ways to verify driving functions are therefore being sought.

Towards Scenario-Based Certification of Highly Automated

An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions.

Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and

The purpose of this paper is to provide a functional safety assessment of the desired behavior of an automated vehicle equipped with a level 4 ADS according to SAE J3016 [3] and to generate a safe desired behavior if needed. We begin with a survey of the state of the art for safeguarding the desired behavior of a highly automated vehicle in Section

Abstract The automotive industry is experiencing a transition from assisted to highly automated driving. New concepts for validation of Automated Driving System (ADS) include amongst other a shift from a “technology based” approach to a “scenario based” assessment.

AI Security and Risk Management: Strategies for Safeguarding Artificial ...

Besides the expected timeliness, correctness, and consistency of commands, their availability is highly safety-relevant for an SAE L4 function. Additionally, an ADI architecture shall foresee self-diagnostic mechanisms and shall support detecting and handling functional insufficiencies (including, but not limited to the perception functions). Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System Abstract— Advances in automated driving are creating new challenges for product development in the automotive industry and continuously driving up the cost of product verification and validation. Modern automated driving systems (ADS) must safely handle a considerable number of driving scenarios in compliance with the Safety of the Intended Functionality (SOTIF)

The Autonomous is an initiative and open platform bringing together leading executives and experts of the mobility ecosystem to align on subjects relevant to the safety of autonomous driving (AD

Abstract Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and training of AI-functions, safety measures and metrics, risk analysis, safety goals and safety requirements.

Scenario-based testing methods in combination with AI-based exploration of high-dimensional scenario parameter spaces represent a new way to automatically generate new unknown and critical test cases for driving functions to be developed. In addition, a cross-manufacturer method for safeguarding highly automated driving functions was created. Services for Securing Automated Driving In the PEGASUS project, fka has developed a database that can be used to make relevant traffic scenarios usable for safeguarding purposes. This document summarizes a vision-based perception system used to guide an automated harvester through fields of alfalfa hay. The system tracks the boundary between cut and uncut crop, detects the end of crop rows, and identifies obstacles. It has been tested successfully at multiple sites, guiding the harvester at speeds up to 4.5 mph while cutting over 60 acres.

Such systems replace human perception and decision-making by employing highly sophisticated solutions based on electronics, IT, and AI. This case study investigates the challenges with context definitions for the development of perception functions that use machine learning for automated driving and shows that there is a lack of standardisation for context definitions across the industry.

Scenario-based testing Under representative conditions, existing approaches such as statistical, distance-based proof of safety would require billions of test kilometers before technology introduction to the market [1]. New methods need to be developed to provide proof of safety for highly automated driving functions. Making the safety of AI-based function modules for highly automated driving verifiable

Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and training of AI-functions, safety measures and metrics, risk analysis, safety goals and safety requirements. The paper presents the overall approach of the German research project

The latter enables a hyperscale simulation of the automated driving function in the extracted scenarios, allowing to decrease the development time and increase the robustness of the software under test. Generative-AI: Generative AI breeds art, music, and code based on extensive data. Its models, such as GANs and transformers, can imitate and understand patterns that combine new outputs from old ones, so that this may assist creativity or automate labor.