Galia Weidl

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• TeknD
• Research Professorship at Aschaffenburg University of Applied Sciences

In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual...

Autonomous driving (AD) technology has seen significant advancements in recent years; however, challenges remain, particularly in achieving reliable performance under adverse weather conditions such as heavy fog. In response, we propose a multi-class fog density classification approach to enhance the AD system performance. By categorizing fog densi...

Traffic congestion has been a major concern in urban areas due to its strong impact on various social, economic, and human safety sectors. Understanding the relationship and analyzing the trends and patterns between congestion and accidents can strengthen the strategy for reducing traffic congestion. Research on causes of accidents and their impact...

The assessment of affective states in online learning often relies on various devices, such as sensors, which indicate physiological reactions of a person's emotional state. It is important to note that not every university can afford the sensors and devices required for this purpose. Furthermore, not every student may be willing to monitor their e...

This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been cru...

The CARLA simulator (Car Learning to Act) serves as a robust platform for testing algorithms and generating datasets in the field of Autonomous Driving (AD). It provides control over various environmental parameters, enabling thorough evaluation. Development bounding boxes are commonly utilized tools in deep learning and play a crucial role in AD a...

Autonomous Driving (AD) technology has rapidly advanced in recent years. Some challenges remain, particularly in ensuring robust performance under adverse weather conditions, like heavy fog. To address this, we propose a multi-class fog density classification approach to enhance the performance of AD systems. By dividing the fog density into multip...

Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, wh...

In this paper, we propose a classification model constructed withan algorithm based on Object-Oriented Bayesian networks (OOBN)to determine the learning style of a student. For this, the Felder-Silverman Learning Style Model (FSLSM) is used, which is basedon the Index of Learning Style (ILS) questionnaire. The idea isto use the answers to the quest...

Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of autom...

We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features,handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the au...

This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires...

This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires...

We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features, handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the a...

This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive...

This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive...

Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur Lückenbewertung für Spur-wechselmanöver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote.

Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations b...

An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their...

An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their...

The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The d...

This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fuse...

This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy sc...

We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncert...

In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselmanövern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zunächst werden die zur Erkennung von Spurwechselvorgängen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das...

IntroductionA methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion

To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presen...

This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enable...

The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quanti...

The developed methodology for Root Cause Analysis (RCA) demonstrates a decision support tool evaluating the process state based on both qualitative and quantitative information. The presented RCA system uses the available data to extract the most probable root causes and proposes an action sequence. The learning ability of the system allows its seq...

The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been appl...

We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on th...

The developed methodology for root cause analysis (RCA) demonstrates a decision support tool evaluating the process state based on both qualitative and quantitative information. The presented RCA System uses the available data to extract the most probable root causes and proposes an action sequence. The learning ability of the system allows its se...

We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-me...

We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maint...

We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information s...

We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and o...

In this thesis we study Bayesian networks (BN) and influence diagrams as tools for early risk assessment of abnormality, root cause analysis (RCA) and decision support (DS) on efficient sequence of corrective actions. We discuss a methodology, which is suitable for operation control of large-scale industrial processes. It should be understood as a...

We propose a methodology for Root Cause Analysis (RCA), allowing fast and flexible decision support for operators, maintenance staff and process engineers in pulp and paper industry. RCA can identify non-obvious process problems and is therefore a powerful complement to normal automatic control. The general methodology is applied to a continuous di...

In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Vis...

We consider in details the dual models for the Goldstone mesons (pions) scattering in the presence of the explicit chiral symmetry breaking caused by non-zero current quark mass. New method of incorporation of the quark masses into the dual model is suggested. In contrast to the previously considered in the literature methods, the dual amplitude ob...

We consider a critical composite superconformal string model to desribe hadronic interactions. We present a new approach of introducing hadronic quantum numbers in the scattering amplitudes. The physical states carry the quantum numbers and form a common system of eigenfunctions of the operators in this string model. We give explicit constructions...