Sep 2026· SAE technical papers on CD-ROM/SAE technical paper series· 24 references
Abstract
Credibility of simulation data has always been fundamental in aerodynamic vehicle development, as a significant amount of early design phase work is conducted virtually before a physical test property is made. As the automotive industry pivots toward artificial intelligence and machine learning techniques to assist in aerodynamic development, training these models with simulation data requires a comprehensive understanding of the accuracy and validity of the underlying simulation. It is critical these systems are trained from reliable data with a full understanding of both the limitations and predictive performance of the computational fluid dynamics (CFD) process and the wind tunnel facility it is benchmarked against. Validation and verification studies have been a long-established set of guidelines to determine if the simulation model appropriately reflects reality (validation) or if it has been set with robust numerical schemes, mesh settings, or boundary conditions (verification). The work presented here shows a comprehensive validation study with more than 400 test configurations and 18 vehicle properties. It evaluates Reynolds-averaged Navier–Stokes (RANS) and detached eddy simulation (DES) approaches using moving reference frame (MRF) and rigid body motion (RBM) to account for wheel rotation and comparing STAR-CCM+ CFD process and the FKFS Aeroacoustic Wind Tunnel (AAWT). The results demonstrate that DES—particularly when wheel rotation is modeled using RBM—provides the highest overall predictive performance, with a drag accuracy from −2% to +4% for 80% of cases with corrections applied, which gets to ±2% for over 95% cases with an additional calibration step. A metric-based assessment criterion that combines key performance metrics into a single detection event (DE) score derived from failure mode effects analysis (FMEA) principles is proposed with an example shown for the 2021 Range Rover Velar. The benefit being that it removes a more judgement-based, qualitative approach, aiding toolset selection and methods development gaps.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
Progress in the field of artificial intelligence has been accelerating rapidly in the past two decades. Various autonomous systems from purely digital ones to autonomous vehicles are being developed and deployed out on the field. As these systems exert a growing impact on society, ethics in relation to artificial intelligence and autonomous systems have recently seen growing attention among the academia. However, the current literature on the topic has focused almost exclusively on theory and more specifically on conceptualization in the area. To widen the body of knowledge in the area, we conduct an empirical study on the current state of practice in artificial intelligence ethics. We do so by means of a multiple case study of five case companies, the results of which indicate a gap between research and practice in the area. Based on our findings we propose ways to tackle the gap.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.
Ville Vakkuri, P. Abrahamsson· International Conference on...· 39 citations· ⚡2
Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· Conference on Technology Eth...· 27 citations· ⚡3
