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Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
Die vorliegende Studie untersucht Entwicklungen und Trends im Nachhaltigkeitscontrolling, insbesondere hinsichtlich der strategischen Bedeutung der fünf Stufen der Nachhaltigkeit, des Einflusses von Stakeholdergruppen, Zielsetzungen und Instrumenten anhand von drei Studien des Fachkreis Green Controlling for Responsible Business und gibt Implikationen für die Controlling- und Unternehmenspraxis.
In this article, we contribute to the longstanding debate among economists regarding the question of “nature or nurture” with respect to economics students’ attitudes toward various allocation mechanisms for a scarce resource. While previous research starts the debate by beginning with first-year economics students, we aim to evaluate pre-firstyear individuals, i.e., school pupils. Drawing on the seminal works of Haucap, J., & Just, T. (2010). Not guilty? Another look at the nature and nurture of economics students. European Journal of Law and Economics, 29(2), 239–254 and Frey, B. S., Pommerehne,W.W., & Gygi, B. (1993). Economics indoctrination or selection? Some empirical results. The Journal of Economic Education, 24(3), 271–281, we investigate a sample of pupils ranging from the 5th to the 13th grades to determine whether pupils are “born economists” (nature), develop economic thinking (nurture), or both. We find that young individuals start to think differently in early grades and that their thinking and attitudes are shaped differently throughout their school careers, thereby providing support for the effects of both nature and nurture. Our findings show that school time impacts fairness judgments, particularly regarding price mechanisms. Regarding learning or indoctrination, we find that economics-inclined pupils are positively affected by lessons in economics in school, while pupils who are economics-averse draw completely diametric conclusions from economics lessons, thereby exhibiting increased disapproval of price allocation over the course of these classes and increased approval of the first come, first served and governmental action mechanisms.Moreover, we find strong effects of gender and migration background in this context. This study is the first to elucidate the development of economic thinking in 5th–13th grade pupils. Our results are important for economists, educators,
and researchers because they can serve as a starting point for subsequent investigations in this under-researched field.
Evaluating the impact of deviating technical standards on business processes, trade and innovation
(2023)
Surrounding the increasingly intense discussions about the emergence of new global standardization regimes in context of China’s rise as a dominant standardization power, there has been much talk about countries purposefully using deviating national standards to impose trade barriers. The discussion of whether and to what degree technical standards deviate from international standards and how this affects business, trade, innovation and the standard system is of global relevance. As research about the impact of deviating technical standards is still strongly underrepresented in the academic community, this research analyses the different “degrees” of deviation and the respective impact of minor or negligible deviation and strong deviation on businesses trading in a global context. By using a mixed research method based on literature review, analysis of standard documents and semi-structured interviews, this study discusses peculiarities and challenges associated with deviating technical standards. This is of relevance with regards to international trade and especially trade with countries that became increasingly important players in the international standardization regime. Our research will therefore add further insights to a better understanding of the close linkage between economic growth and standardization. This paper further highlights how deviating technical standards impact companies around the globe and how these companies could use a newly developed risk indicator to not only engage in the standard game but also to better assess consequences.
The massive shift to working from home during the Covid-19 pandemic triggered discussions about its potential impact on the future demand for office space and the risk it poses to the performance of office markets. Against this background, the goal of this paper is to investigate the link between working from home and the evolution of key indicators of office occupier markets across Europe over the past three decades. Based on the data from Eurostat and CBRE, the paper uses panel regression to investigate the temporal as well as cross-sectional relationships between the share of the workforce working from home and office rents and vacancy rates in major cities. The results are interesting in several ways. Firstly, changes in the share of employees working from home did not appear to have any significant impact on the evolution of rents or vacancy rates over time. However, occasional homeworking was significant in explaining cross-sectional differences in office market indicators. Moreover, contrary to the initial expectations, higher share of employees occasionally working from home appeared to be associated with stronger performance of the respective office market. As explanation, the paper proposes a hypothesis that this was due to working from home being only one aspect of broader changes in the office work environment and related socio-economic trends that had a net beneficial effect on office occupier markets. Although the results refer to historical developments and may not be fully applicable to the current context of the pandemic, they highlight the need to consider working from home in a broader perspective of office occupier trends and ways of working.
Direktinvestitionen
(2001)
Normung galt lange als ein rein technisches Thema für ausgewiesene Spezialisten. Neuerdings erhält das Thema zusätzliche Aufmerksamkeit durch Vertreter aus industrie- und geopolitischen Fachkreisen, insbesondere aufgrund des Entwicklungsprojekts zur Seidenstraße. Die Autorinnen skizzieren den Erneuerungsbedarf für die Normungswelt und fordern mehr Innovation, um den wachsenden Herausforderungen in der internationalen Normung entgegen zu treten.
This experimental study analyzes how a key factor, information load, influences decision making in escalation situations, i.e., in situations in which decision mak- ers reinvest further resources in a losing course of action, even when accounting information indicates that the project is performing poorly and should be discontin- ued. This study synthesizes prior escalation research with information overload and investigates how different levels of information load influence the escalation of com- mitment. Our findings reveal a U-shaped effect of information load: When decision makers face negative feedback, a higher information load mitigates the escalation tendency up to a certain point. However, beyond this point, more information rein- forces the escalation tendency. Moreover, we find that the type of feedback affects self-justification, and we find a negative and significant interaction between informa- tion load and self-justification in negative-feedback cases. Thus, studies investigat- ing escalation of commitment should control for self-justification and information load when utilizing high levels of information load. Finally, in the positive-feedback condition, higher information load encourages decision makers to continue promis- ing courses of action, i.e., increases decision-making performance.
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
(2022)
In this study, we empirically show the dynamics of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) to consist of an almost equally divided combination of stochastic and deterministic chaos, rendering the series unpredictable after expiration of the Lyapunov time, resulting in futile forecasting attempts. We achieve a clear distinction of the true nature of the underlying time series dynamics by applying a novel and combinatory chaos analysis framework comparing the wavelet-filtered S&P 500 returns with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. Furthermore, we are the first to show the strange attractor of especially the daily-frequented S&P 500 return system graphically via Takens´ embedding and by spectral embedding in combination with Laplacian Eigenmaps. Finally, we critically discuss implications and future prospects in terms of financial forecasting.