Requirements Strategy for Managing Human Factors in Automated Vehicle Development
- URL: http://arxiv.org/abs/2405.18838v1
- Date: Wed, 29 May 2024 07:37:57 GMT
- Title: Requirements Strategy for Managing Human Factors in Automated Vehicle Development
- Authors: Amna Pir Muhammad, Alessia Knauss, Eric Knauss, Jonas Bärgman,
- Abstract summary: The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs)
Ensuring that HF knowledge is considered continuously throughout the AV development process is essential for several reasons, including efficacy, safety, and acceptance of these advanced systems.
This paper applies the concept of Requirements Strategies as a lens to the investigation of HF requirements in agile development of AVs.
- Score: 4.419836325434071
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs). Ensuring that HF knowledge is considered continuously throughout the AV development process is essential for several reasons, including efficacy, safety, and acceptance of these advanced systems. However, it is challenging to include HF as requirements in agile development. Recently, Requirements Strategies have been suggested to address requirements engineering challenges in agile development. By applying the concept of Requirements Strategies as a lens to the investigation of HF requirements in agile development of AVs, this paper arrives at three areas for investigation: a) ownership and responsibility for HF requirements, b) structure of HF requirements and information models, and c) definition of work and feature flows related to HF requirements. Based on 13 semi-structured interviews with professionals from the global automotive industry, we provide qualitative insights in these three areas. The diverse perspectives and experiences shared by the interviewees provide insightful views and helped to reason about the potential solution spaces in each area for integrating HF within the industry, highlighting the real-world practices and strategies used.
Related papers
- Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)
RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.
Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
arXiv Detail & Related papers (2025-02-08T06:50:47Z) - Safety at Scale: A Comprehensive Survey of Large Model Safety [299.801463557549]
We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats.
We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
arXiv Detail & Related papers (2025-02-02T05:14:22Z) - An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance.
SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring.
An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
arXiv Detail & Related papers (2024-10-23T09:14:57Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.
Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.
However, the deployment of these agents in physical environments presents significant safety challenges.
This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents [12.593620173835415]
Foundation Model (FM)-based agents are revolutionizing application development across various domains.
We present a comprehensive taxonomy of runtime guardrails for FM-based agents to identify the key quality attributes for guardrails and design dimensions.
Inspired by the Swiss Cheese Model, we also propose a reference architecture for designing multi-layered runtime guardrails for FM-based agents.
arXiv Detail & Related papers (2024-08-05T03:08:51Z) - Defining Requirements Strategies in Agile: A Design Science Research Study [4.110602799032192]
Research shows that many of the challenges currently encountered with agile development are related to requirements engineering.
This paper investigates critical challenges that arise in agile development from an undefined requirements strategy.
arXiv Detail & Related papers (2024-05-29T07:57:32Z) - Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices [4.419836325434071]
It is important to consider human factors (HF) knowledge when developing automated vehicles (AVs) to make them safe and accepted.
This study explores the current practices and challenges of the automotive industries for incorporating HF requirements during agile AV development.
arXiv Detail & Related papers (2024-05-29T07:48:43Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - Safety of autonomous vehicles: A survey on Model-based vs. AI-based
approaches [1.370633147306388]
It is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs.
It is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches.
This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks.
arXiv Detail & Related papers (2023-05-29T08:05:32Z) - Mitigating Risks in Software Development through Effective Requirements
Engineering [0.0]
This article provides an overview of the importance of requirements gathering in secure software development.
It explains the crucial role of Requirements Engineers in defining and understanding the customer's needs and desires.
The article emphasizes the need to mitigate the risks of vagueness and ambiguity early on and provides techniques for evaluating, negotiating, and prioritizing requirements.
arXiv Detail & Related papers (2023-05-09T23:12:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.