Further Steps towards Driver Modeling According to the Bayesian Programming Approach

Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process.

Although there are numerous ergonomics assessment methods available for assessing safety, comfort, and performance of human-product interactions, there is a lack of comprehensive and widely available computational human-centered design framework that brings human factors principles into engineering design. In existing human-centered design approaches, computational assessment of human-product interaction is not explicitly considered as part of the early design phase, but rather it is addressed downstream in the design process through traditional human factors engineering strategies, often, includes physical prototyping and usability studies with extensive human-subject data collection. We hypothesize that digital human modeling can be utilized not only as an ergonomics evaluation tool but also as an engineering design methodology to embrace form and function during the early phases of the development. The digital human modeling approach has the potential to be a merger between the form and functionality aspects of product design by focusing on human needs, abilities, and limitations. This paper introduces a novel human-in-the-loop methodology which injects human factors engineering principles via digital human modeling into a computational design environment to assess the safety and performance of human-product interactions early in design.

Lift-off velocity of saltating sand particles in wind-blown sand located at 1.0 mm above the sand bed surface was measured using a phase Doppler particle analyzer in a wind tunnel. The results show that the probability distribution of lift-off velocity can be expressed as a lognormal function, while that of lift-off angle follows an exponential function. The probability distribution of lift-off angle conditioned for each lift-off velocity also follows an exponential function, with a slope that becomes steeper with increasing lift-off velocity. This implies that the probability distribution of lift-off velocity is strongly dependent on the lift-off angle. However, these lift-off parameters are generally treated as an independent joint probability distribution in the literature. Numerical simulations were carried out to investigate the effects of conditional versus independent joint probability distributions on the vertical sand mass flux distribution. The simulation results derived from the conditional joint probability distribution agree much better with experimental data than those from the independent ones. Thus, it is better to describe the lift-off velocity of saltating sand particles using the conditional joint probability distribution. These results improve our understanding of saltation processes in wind-blown sand.

Generative design uses artificial intelligence-driven algorithms to create and optimize concept variants that meet or exceed performance requirements beyond what is currently possible using the traditional design process. However, current generative design tools lack the integration of human factors, which diminishes the efforts to understand and inject a broad set of human capabilities, limitations, and potential emotional responses for future human-centered product and service innovation. This paper demonstrates collaborative research in formulating a human-centered generative design framework that injects human factors early in the design for quick-and-dirty concept creation and evaluation. Three case studies overviewing our ongoing multidisciplinary research efforts in synthesizing human and mechanical attributes are presented. The results show that the framework has the potential to enhance human factors representation within generative design workflow. Strategies from a computational design perspective, such as data-driven generative design, digital human modeling, and mixed-reality validation, are discussed as alternative approaches that could be implemented to augment designers.

The concept of focus point preview is proposed, and fractional calculus is introduced to driver model to build focus point preview driver model. A formula for calculating lateral error is given, where the weight coefficients of fractional calculus are designed to imitate the driver’s focus preview property. The relationship between the speed and the order of fractional calculus is studied. A driver-vehicle-road simulation system is set up to illustrate the performances of the proposed preview model. The S-type road is used to test the model in the case of continuous small curvature turns and the Shanghai F1 track model is used as the case that automobile passing large curvature curve. The performances are evaluated from two aspects: path tracking effect and vehicle dynamic responses. It is concluded that, as the speed of the vehicle increases, the optimal order of fractional integral increases, so that the order is seen as the degree of driver’s attention. What is more, in the case of large curvature, the path tracking performance is improved by increasing the corresponding fractional order. Simulations results also show that, compared with the single-point preview model, the performances of the focus point preview model are better. On the one hand, the proposed driver model can be used to control the vehicle steering and path tracking. On the other hand, fractional calculus is used to reveal the driver preview property and the order is given a certain physical meaning, which is conducive to the development of fractional calculus applications.

Joint probability distribution of winds and waves from wave simulation of 20 years (1989-2008) in Bohai Bay

Maps are usually better abstractions of reality than other media and allow easier perception of included spatial information. This fact makes maps convenient for several specialised societal purposes, including emergency management and transportation, among others.It is very important to understand the processes of reading and understanding map representations during a variety of situations. There are significant differences in map use by various users. Differences can be caused by variations in cartographic method or lack of time. Therefore, specific situations need specific map representations. This article addresses the problem of testing the practice of reading and understanding maps. The evaluation emphasises the externality of the testing process, meaning that results cannot be based only on subjective opinions of tested participants.Results of the testing will be used for construction of suitable maps for various situations as well as the development of a software tool that will enable creation of standardised testing sets. Additionally, standardised research methodology will be developed to enhance exploration of cognitive map reading processes. Results are validated by statistical analysis tools using a combination of quantitative and qualitative methods.Fundamentally, this work provides insights into the processes of perception by different groups of users, allowing increased map information transmission efficiency, especially important during crisis situations.KeywordsTest SubjectCognitive StyleIntuitive JudgementCartographic TheoryCartographic MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As one move towards driverless cars, there will always be a big worry of how autonomous cars would behave in the presence of vehicles driven by humans. In the co-existence model, it is essential for autonomous systems to 'understand' the behavior and gazing patterns of the drivers across different road turns and traffic lights. It is essential to understand that each road turn in a city is different due to angle of turn, building environment, etc. Hence, one needs to understand the gaze patterns of drivers across different turns and traffic lights. This paper is a long-drawn effort for measuring driver's observability and deriving driver's observance pattern in real-time. In this regard, an experimental model is provided and clustering-based technique is applied that would measure driver observability. More than 100 segmented readings were extracted from the video of five different vehicles and drivers under two different road conditions. It was observed that - while taking left or right turns and waiting in traffic light, the focus of drivers in front of their cars also changes considerably, with significant variation in the driver's gaze point, both horizontally and vertically. This is a important pattern/result in the design of adaptive driver assistance system and future driverless cars.

BackgroundIncreasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken towards sDHT usability evaluation have varied markedly.ObjectiveTo elucidate the current landscape of studies reporting data related to sDHT human factors, human-centered design, and/or usability.MethodsWe conducted a systematic scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and/or usability were reported. Following a systematic screening process, we extracted the study design; participant sample; the sDHT(s) used; the method(s) of data capture; and the type(s) of usability-related data captured.ResultsOur literature search returned 442 papers, of which 85 were found to be eligible and 83 were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 were wearable tools while the remaining 23 were ambient tools. The majority of studies (n=55; 66%) reported summative evaluations of final-design sDHTs. Almost all studies (n=82; 98%) captured data from targeted end-users, but only 18 (22%) captured data from additional users such as carepartners or clinicians. User satisfaction and ease of use were evaluated for >80% of sDHTs; however, learnability, eficiency, and memorability were reported for only 11 (13%), 4 (5%), and 2 sDHTs (2%), respectively. Fourteen sDHTs (17%) were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) and/or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 25% of all studies and 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 17% of studies).ConclusionsBased on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: 1) Consider in-depth assessment of technology usability beyond user satisfaction and ease of use; 2) Expand recruitment to include important user groups such as clinicians and carepartners; 3) Report the rationale for key study design considerations including the sample size; and 4) Provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

One of the major challenges of user experience (UX) design in autonomous driving (AD) is to investigate the emerging expectations and needs of future passengers. Working with foresight into future interactions requires interdisciplinary collaboration that enables new modes of working. In our everyday work and throughout the sound interaction design process, we are often challenged by the lack of exploratory methodologies for involving human insights early on in the sound interaction design process. This chapter draws on the interviews with experts in sound design and UX domains, the research project, Sound Interaction for Intelligent Cars (SIIC), as well as daily product development work at the Volvo Car Corporation (VCC) to address this gap. We characterise what we call the classic approach to sound interaction design and outline methodologies used at different stages of the design thinking process for generating and translating UX insights into sonic interaction solutions. By this, we broaden the way of practicing participatory sound UX for AD development and respond to the challenge by incorporating existing exploratory design research methodologies, such as sensory ethnography, soundwalk, co-creation, ideation, and rapid prototyping.KeywordsAutonomous drivingUser experienceSonic interaction designSensory ethnographyParticipatory designDesign thinking

Although automated vehicles (AVs) are increasingly capable of handling well-structured traffic scenarios, navigating complex urban environments with large numbers of unpredictable human road users, particularly pedestrians, remains a major challenge. Human drivers often rely on subtle social cues and responsive behaviors, such as the rolling stop, to navigate such settings. Traditionally, a rolling stop refers to decelerating without fully stopping at stop-sign-controlled intersections, also known as the “California Roll”. This work builds on that concept and applies a human-centered design (HCD) process, involving exploration, conceptualization, prototyping, and evaluation, to expand the rolling stop as an implicit communication strategy for AVs to navigate multi-pedestrian urban environments. In this context, a rolling stop is defined as a scenario-dependent, low-speed motion pattern in which the vehicle avoids a full stop while continuously adapting to dynamic elements such as pedestrians and road infrastructure. The feature was prototyped and evaluated using the F1Tenth autonomous racing car, demonstrating the feasibility of rolling stop for AV-pedestrian interactions and the effectiveness of F1Tenth for early-stage AV development. Future research should generalize the rolling stop application to diverse and dynamic urban environments while considering varying pedestrian characteristics and interaction contexts.

Adolescents 360 (A360) was a 4.5-year project working directly with young people to increase demand for, and voluntary uptake of, modern contraception among adolescent girls aged 15 to 19 years. A360 utilized human centered design (HCD) to create four adolescent sexual and reproductive health (ASRH) interventions across three countries -Smart Start in Ethiopia,Kuwa Mjanja in Tanzania,Matasa Matan Arewa (MMA) in northern Nigeria, and9ja Girls in southern Nigeria. A360’s interventions tap into girls’ aspirations and position contraception as a tool that can support them in pursuing their life goals. As A360 transitioned from its first program phase into its follow-on in 2020, the project examined what it had accomplished, where it had failed, and what it had learned in the process, with the goal of contributing to the global evidence base and building on these lessons in its follow-on program. A360 draws out five key lessons in this publication. These lessons speak to 1) the value of A360’saspirational program components and the need to meaningfully support girls to pursue their life goals holistically; 2) the necessity of taking a consistent and rigorous approach to improving theenabling environment for contraceptive use to promote transformative change; 3) the need to find program and measurement approaches that respond to girls’ unique patterns of sexual activity, and supportcontraceptive continuation; 4) the usefulness ofcontinuous program improvement during implementation to maintain a user-centered focus and create a culture of curiosity and innovation; and 5) the tension between designing for users andbeginning with program sustainability in mind from the outset. A360 continues to grow in its understanding of what it takes to support sustained, transformative, holistic change for adolescent girls and commits to openness and transparency regarding successes and failures during its next project phase.

A priori structural information is incorporated into determinantal phasing techniques to improve phase prediction accuracy in resolution ranges where atomic or isotropic group-scatterer assumptions are not valid. For this purpose a conditional joint probability distribution to triplet order for any set of normalized structure factors of space groups P1 and P\bar 1 is derived. The covariance of two normalized structure factors from the original set is calculated. A more general conditional joint probability distribution, involving covariance matrices of any order, is further derived. Numerical tests are performed employing ideal models consisting of several atomic groups of known stereochemistry but with random positions and orientations. The results indicate that the inclusion of stereochemical information improves the accuracy of phase prediction. The relative merit of this strategy in either one of or both normalization and covariance calculations for different resolutions is discussed.

Developing autonomous vehicles is technically complex and up to now research has focused on technical improvement and operative safety. As the level of automation increases the role of the driver will change; from controlling every movement of the vehicle into becoming an operator/passenger. Little is known about how this new context will affect the social experiences with and within the vehicle. This workshop focuses on three different kinds of social experience and socializing, namely; between other road users and the autonomous car, the social activities taking place within the autonomous car, and lastly the relationship between the car and the operator. The workshop aims at exploring possible practices, research and design directions of autonomous vehicles in relation to these social experiences. A human-centered design approach is the core of the workshop, with playful field excursions and ideation sessions.

Problem definition: Motivated by the significance of side information in numerous operations management problems, this paper studies conditional stochastic optimization to enable more informed decisions. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. Decision makers who adapt their decisions according to the observed side information solve a stochastic optimization problem where the objective function is specified by the conditional expectation of the random cost. If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using kernel regression. Although this approximation scheme has found successful applications in diverse decision problems under uncertainty, it is largely unknown whether the scheme can provide any reasonable out-of-sample performance guarantees, and how such statistical guarantees can guide the decision-making process. Methodology/results: We employ the Nadaraya–Watson kernel regression for data-driven approximation of the conditional expectation and leverage moderate deviations theory to establish its performance guarantees. Our analysis and resultant statistical bounds motivate the use of a conditional standard deviation regularization scheme to enhance out-of-sample performances. As the designed regularization scheme leads to a nonconvex optimization problem, we further adopt ideas from distributionally robust optimization to obtain tractable formulations. We examine our proposed models on portfolio optimization, inventory management, and wind energy commitment problems. The numerical results demonstrate the effectiveness of our proposed regularization scheme. Managerial implications: Our paper illustrates the importance of side information in real-world decision-making problems. Incorporating side information through a regularized Nadaraya–Watson scheme offers managers a robust framework to enhance decision making under uncertainty. The theoretical guarantees provide guidance on the number of samples required to obtain high-quality solutions and how to optimally adjust the regularization parameter. For problem instances with high-dimensional covariates, we further present a simple dimensionality reduction procedure that helps improve the sample complexity of the scheme. All our proposed formulations are concise and straightforward for the operations manager to implement using any popular programming language interfaced with standard off-the-shelf solvers. Funding: Y. Wang was supported by the National Natural Science Foundation of China [Grant 72501204] and the Fundamental Research Funds for the Central Universities. G. A. Hanasusanto was funded by the National Science Foundation [Grants CCF-2343869 and ECCS-2404413]. C. P. Ho was supported by the Research Grants Council [General Research Fund 11508623] and the CityUHK Start-Up Grant [9610481]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0997 .