Challenges of Virtual Validation and Verification for Automotive Functions
- URL: http://arxiv.org/abs/2508.14747v1
- Date: Wed, 20 Aug 2025 14:45:03 GMT
- Title: Challenges of Virtual Validation and Verification for Automotive Functions
- Authors: Beatriz Cabrero-Daniel, Mazen Mohamad,
- Abstract summary: We conducted a workshop with experts in the field, allowing them to brainstorm key obstacles.<n>The experts identified 17 key challenges, along with proposed solutions.<n>Many of the identified problems already have known solutions.
- Score: 0.20462238493547852
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Verification and validation of vehicles is a complex yet critical process, particularly for ensuring safety and coverage through simulations. However, achieving realistic and useful simulations comes with significant challenges. To explore these challenges, we conducted a workshop with experts in the field, allowing them to brainstorm key obstacles. Following this, we distributed a survey to consolidate findings and gain further insights into potential solutions. The experts identified 17 key challenges, along with proposed solutions, an assessment of whether they represent next steps for research, and the roadblocks to their implementation. While a lack of resources was not initially highlighted as a major challenge, utilizing more resources emerged as a critical necessity when experts discussed solutions. Interestingly, we expected some of these challenges to have already been addressed or to have systematic solutions readily available, given the collective expertise in the field. Many of the identified problems already have known solutions, allowing us to shift focus towards unresolved challenges and share the next steps with the broader community.
Related papers
- Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey [48.53273952814492]
Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains.<n>Applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification.
arXiv Detail & Related papers (2025-05-06T10:53:58Z) - Challenges of Requirements Communication and Digital Assets Verification in Infrastructure Projects [45.16509948770781]
Poor communication of requirements between clients and suppliers contributes to project overruns.<n>Our research aim to explore the processes and associated challenges with requirements activities.
arXiv Detail & Related papers (2025-04-29T07:52:08Z) - Intrinsic Barriers to Explaining Deep Foundation Models [17.952353851860742]
Deep Foundation Models (DFMs) offer unprecedented capabilities but their increasing complexity presents profound challenges to understanding their internal workings.<n>This paper delves into this critical question by examining the fundamental characteristics of DFMs and scrutinizing the limitations encountered by current explainability methods.
arXiv Detail & Related papers (2025-04-21T21:19:23Z) - SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories [55.161075901665946]
Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories.
Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development.
We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
arXiv Detail & Related papers (2024-09-11T17:37:48Z) - Foundational Challenges in Assuring Alignment and Safety of Large Language Models [171.01569693871676]
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs)
Based on the identified challenges, we pose $200+$ concrete research questions.
arXiv Detail & Related papers (2024-04-15T16:58:28Z) - Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors.
This paper aims to conduct a comprehensive review and analysis of object detectors in open environments.
We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
arXiv Detail & Related papers (2024-03-24T19:32:39Z) - Some challenges of calibrating differentiable agent-based models [0.0]
Agent-based models (ABMs) are promising approach to modelling and reasoning about complex systems.
Their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks.
arXiv Detail & Related papers (2023-07-03T15:07:10Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - Unsupervised Person Re-Identification: A Systematic Survey of Challenges
and Solutions [64.68497473454816]
Unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID.
Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning.
This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions.
arXiv Detail & Related papers (2021-09-01T00:01:35Z) - Machine Learning (In) Security: A Stream of Problems [17.471312325933244]
We identify, detail, and discuss the main challenges in the correct application of Machine Learning techniques to cybersecurity data.
We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions.
We present how existing solutions may fail under certain circumstances, and propose mitigations to them.
arXiv Detail & Related papers (2020-10-30T03:40:10Z) - An empirical investigation of the challenges of real-world reinforcement
learning [29.841552004806932]
We identify and formalize a series of independent challenges that embody the difficulties that must be addressed for RL to be commonly deployed in real-world systems.
We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems.
arXiv Detail & Related papers (2020-03-24T11:05:41Z)
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.