Coordinating Stakeholders in the Consideration of Performance Indicators and Respective Interface Requirements for Automated Vehicles
- URL: http://arxiv.org/abs/2603.19492v1
- Date: Thu, 19 Mar 2026 21:44:13 GMT
- Title: Coordinating Stakeholders in the Consideration of Performance Indicators and Respective Interface Requirements for Automated Vehicles
- Abstract summary: This paper presents a process for coordinating stakeholders in their consideration of performance indicators and respective interface requirements for automated vehicles.<n>Our approach is based on the experience gained from applying the process in the autotech.agil project.
- Score: 68.70876285311324
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a process for coordinating stakeholders in their consideration of performance indicators and respective interface requirements for automated vehicles. These performance indicators are obtained and processed based on the system's self-perception and enable the realization of self-aware and self-adaptive vehicles. This is necessary to allow SAE Level 4 vehicles to handle external disturbances as well as internal degradations and failures at runtime. Without such a systematic process for stakeholder coordination, architectural decisions on realizing self-perception become untraceable and effective communication between stakeholders may be compromised. Our process-oriented approach includes necessary ingredients, steps, and artifacts that explicitly address stakeholder communication, traceability, and knowledge transfer through clear documentation. Our approach is based on the experience gained from applying the process in the autotech.agil project, from which we further present lessons learned, identified gaps, and steps for future work.
Related papers
- From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents [69.07911499189129]
We introduce KAPRO, a framework that evaluates cognitive-behavioral alignment by decoupling an agent's metacognitive judgment (Knowing) from its spontaneous execution (Acting)<n>Experiments show that self-awareness capability is strongly correlated with task success but degrades sharply in internal-capability settings.<n> proprietary and reasoning-oriented models demonstrate more reliable cognitive gating.
arXiv Detail & Related papers (2026-06-09T17:17:08Z) - Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
arXiv Detail & Related papers (2026-05-17T10:26:37Z) - Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs [41.48741921431306]
We introduce a proof-of-concept self-adaptive approach that formalises prompt engineering as a cognitive and probabilistic decision-making process.<n>We evaluate the adaptive explanation generation and their qualities, both quantitatively and qualitatively.<n>Our results demonstrate the feasibility of integrating human cognition and user profile's feedback into automated prompt synthesis.
arXiv Detail & Related papers (2026-04-22T21:22:21Z) - Driving Accessibility: Shifting the Narrative & Design of Automated Vehicle Systems for Persons With Disabilities Through a Collaborative Scoring System [0.0]
Underrepresented groups, including individuals with mobility impairments, sensory disabilities, and cognitive conditions, are often overlooked in crucial discussions on system design, implementation, and usability.<n>We aim to shift automated vehicle research and discourse away from this reactive model and toward a proactive and inclusive approach.
arXiv Detail & Related papers (2026-01-06T02:05:25Z) - AutoGraph: A Knowledge-Graph Framework for Modeling Interface Interaction and Automating Procedure Execution in Digital Nuclear Control Rooms [8.210956605746178]
AutoGraph is a knowledge-graph-based framework designed to formalize and automate procedure execution in digitalized NPP environments.<n>This enables the identification of cognitively demanding multi-action steps and supports fully automated execution with minimal operator input.
arXiv Detail & Related papers (2025-05-26T14:02:18Z) - Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
arXiv Detail & Related papers (2025-02-18T17:12:26Z) - Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.266685603250416]
Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces.<n>This article examines how interactive feedback features in ChatGPT's interface afford user participation in LLMs.
arXiv Detail & Related papers (2024-08-27T13:50:37Z) - Interactive Autonomous Navigation with Internal State Inference and
Interactivity Estimation [58.21683603243387]
We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework.
These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents.
Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
arXiv Detail & Related papers (2023-11-27T18:57:42Z) - The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction.
We discuss the implications, strengths, and limitations of different integration principles.
arXiv Detail & Related papers (2023-08-10T17:53:03Z) - OASIS: Automated Assessment of Urban Pedestrian Paths at Scale [16.675093530600154]
We develop a free and open-source automated mapping system to extract sidewalk network data using mobile physical devices.
We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team.
arXiv Detail & Related papers (2023-03-04T01:32:59Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - The Roles and Modes of Human Interactions with Automated Machine
Learning Systems [7.670270099306412]
Automated machine learning (AutoML) systems continue to progress in both sophistication and performance.
It becomes important to understand the how' and why' of human-computer interaction (HCI) within these frameworks.
This review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.
arXiv Detail & Related papers (2022-05-09T09:28:43Z)
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.