Development Of Assistance Systems Research Articles

Braking intensity recognition with optimal K-means clustering algorithm

Recognizing a driver’s braking intensity plays a pivotal role in the development of modern driver assistance and energy management systems; and therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a system for recognizing a driver’s braking intensity based on the pressure produced in the brake master cylinder. In this regard, a model-based, synthetic data generation concept is used to generate the training dataset. This technique involves two closed-loop controlled models: an upper-level longitudinal vehicle dynamics model, and a lower-level brake hydraulic, dynamic model. The adaptive particularly tunable fuzzy particle swarm optimization algorithm is recruited to solve the optimal K-means clustering problem and to determine the best number of clusters and centroids into which the brake pressure data are to be divided. The obtained results reveal that the brake pressure data for a vehicle traveling the new European driving cycle can be best partitioned into two clusters. A driver’s braking intensity may, therefore, be clustered as moderate or intensive. With the ability to automatically recognize a driver’s pedal feel, the system developed in this research could be implemented in intelligent driver assistance systems as well as in electric vehicles equipped with intelligent, electromechanical brake boosters.

Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning.

Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.

Identification and modelling of race driving styles

A good understanding and modelling of the human driver is essential for modern vehicle development, particularly in motorsports, where the race car should fit its driver perfectly. At the same time, an objective assessment and especially imitation of professional race drivers is difficult due to individual driving styles, complex and non-deterministic decision making processes, and small stability margins. In this paper, we present a holistic approach to identify and model individual race driving styles in a robust way. We develop the Driver Identification and Metric Ranking Algorithm (DIMRA) as a data-based method for an in-depth objective analysis and assessment of professional race drivers. Supported by this knowledge, we extend and adapt the imitation learning framework Probabilistic Modeling of Driver Behavior (ProMoD) in order to model race drivers in a complex simulation environment. An evaluation with data from professional race drivers shows the capability of DIMRA to derive metrics which describe human race driving styles, as well as ProMoD to robustly generate competitive laps with human-like controls in a professional motorsport driving simulator. The ability to identify and imitate individual driving styles does not only support the performance optimisation of race cars but could also aid the development of road cars and driver assistance systems in future work.

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

BackgroundAdherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval.MethodsFirst, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology “Ontology for factors influencing therapy adherence to rehabilitation” (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts.ResultsOnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area.ConclusionsWith OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

Risky behaviors, psychological failures and kinematics in vehicle-to-powered two-wheeler accidents: Results from in-depth Chinese crash data

As the use of powered two-wheeler (PTW) becomes increasingly prevalent, PTW accidents are emerging as a major threat to the people’s life and property in China. Understanding the risky behaviors, psychological failures and kinematics in vehicle-to-PTW accidents is an important first step in addressing this issue.Here 69 vehicle-to-PTW accidents captured on video from the Traffic Accident Investigation and Research in China (TAIRC) database are selected and reconstructed. All accidents are categorized into different crash scenarios using a harmonized method. Accident causations are identified from the perspectives of praxeology and psychology. Kinematics characteristics, such as impact speed and relative position, are also analyzed.The results show the crossing accident bundle is the most frequent followed by rear, oncoming and run-up accident bundles, with proportions of 43.48 %, 27.54 %, 11.59 % and 17.39 % respectively. Accident causations of different crash scenarios have great differences whether in accident responsibilities or in psychological failures. For instance, the PTW riders who violate the traffic regulations need to be mainly responsible for most crossing accidents, whereas most rear accidents are blamed on drivers who fail to properly check their mirrors when they turn, turn around or change lanes. From the perspective of psychology, the perception failures encountered by both drivers and riders are a typical causation in crossing accidents, while it is a contributing factor in rear accidents that a failure of prognosis from the rider combined with a failure of perception from the driver. Visual obstruction exists widely in crossing and oncoming accident bundles. The impact speeds of vehicles and PTWs are often less than 40 km/h in all accident bundles. A wider sensing area (field of view = 90°, view detection range = 35 m) should be achieved to more effectively detect the conflict PTWs.These findings about vehicle-PTW accidents provide a stronger support for the development of prevention countermeasures and advanced driver assistance system.

Visual Impairment Assistance System

Visually challenged people have no knowledge of outdoor hazards and require visual aids to avoid threats. Furthermore, they face many difficulties compared to other people in doing their day to day work. Even though they have guide canes to help them it becomes difficult to recognize the faces of the people. They find it difficult to identify the familiar as well as unknown faces. Recognizing familiar faces confidently without anyone’s help is a very difficult task for a blind person. This model uses deep learning techniques that can identify familiar and unknown faces for visually impaired people. Apart from recognizing faces, blind people find it difficult to read simple signboards and important instructions. A few of the examples are the menu cards of the restaurants, Direction boards, locating washrooms, etc. The proposed model will help the visually challenged people to read the images by converting the image into a text and then converting the same text which is read into audio. For converting the image to text, OCR technology was used. Recognizing face is a very important task for the visually challenged people as a failure of this may even lead to life-threatening problems and reading sign boards and other simple text is also very important in the daily life of visually challenged people. Hence the development of this visual impairment assistance system will help the visually challenged people to live a better life.

Natural Walking With Musculoskeletal Models Using Deep Reinforcement Learning

Human gait optimality has been investigated recently, with the development of detailed musculoskeletal models, through trajectory optimization approaches or deep reinforcement learning (DRL). Trajectory optimization studies are limited by the trajectory length and can only generate open-loop solutions. While existing DRL solutions provide closed-loop control policies without trajectory length limit, they either do not evaluate the naturalness of the behaviour, or directly impose experimental tracking data. In this letter, a DRL-based approach is proposed with a nature-inspired curriculum learning (CL) scheme and a neuromechanically-inspired reward function. This approach generates close-to-natural human walking without the aid of experimental data. Our CL scheme is realized by an evolving reward function, targeting simpler behaviours such as standing and stepping first, then gradually refining the gait. The emerged gait from the closed-loop stochastic policy demonstrated a strong correlation with human gait kinematics, with Pearson correlations of 0.95 and 0.83 at the hip and knee, respectively, and higher gait symmetry than two other DRL-based control policies without CL. Our approach was also found to have efficient convergence to walking-capable policy. This approach can facilitate the development of assistive robotic systems by providing a “human” controller, and could enable decentralized adaptation between the agent and the assistive robotic devices.

WITHDRAWN: Design and development of vehicle breakdown assistance System: i-Touch

Abstract The main aim of the current work is to build a platform which provides an immediate local assistance during a vehicle breakdown. A connected environment accessible by both commuters and service providers, specific to a geographical region is designed and investigated. The platform is accessed through a connected cellular device. A dedicated hardware is attached to vehicle, to collect vehicles’ environmental parameters. The information collected along with the type of assistance required, is shared among the local service providers throught elemetry protocol. The real-time practicability of proposed solution is validated through a prototype model. The measured values have been compared with existing solutions in terms of parameters such as, total time required, mobile battery drain and tracked location accuracy. The difference between using a Representational State Transfer (REST) protocol and Telemetry protocol is also equated. The use of dedicated on-board device in comparison to software only solutions or integrated models are also compared. The additional potential areas in which the proposed solution finds applications is also descripted. The interoperability of the proposed system and its caliber to adapt new technologies is also discoursed.

Development of an Ethernet-Based Heuristic Time-Sensitive Networking Scheduling Algorithm for Real-Time In-Vehicle Data Transmission

The rapid development and adaptation of advanced driver assistance systems (ADAS) and autonomous driving increases the burden of in-vehicle networks. In-vehicle networks are now required to provide a fast data rate and bounded delay for real-time operation, while conventional protocols such as controller area networks, local interconnected networks, and FlexRay begin to show limitations. Ethernet-based time-sensitive network (TSN) technology has been proposed as an alternative. TSN is a set of Ethernet standards being developed by the IEEE TSN task group, which aims to satisfy requirements such as real-time operation, stability, and low and bounded latency, and it can be used in automotive, industrial, and aerospace applications. This study introduces several standards for Ethernet traffic scheduling based on TSN technology and proposes a heuristic-based scheduling algorithm for Ethernet scheduling. In addition, three network configurations are simulated using OMNeT++ to show the applicability. The heuristic TSN scheduling algorithm is a straightforward and systematic procedure for practical network designers.

Probabilistic Estimation of Threat Intrusion in Embedded Systems for Runtime Detection

With billions of networked connected embedded systems, the security historically provided by the isolation of embedded systems is no longer sufficient. Millions of new malware are created every month and zero-day attacks are becoming an increasing concern. Therefore, proactive security measures are no longer enough to provide protection to embedded systems. Instead, reactive approaches that detect attacks that can circumvent the proactive defenses and react upon them are needed. Anomaly-based detection is a common reactive approach employed to detect malware by monitoring anomalous deviations in the system execution. Timing-based anomaly detection detects malware by monitoring the system's internal timing, which offers unique protection against mimicry malware compared to sequence-based anomaly detection. However, previous timing-based anomaly detection methods focus on each operation independently at the granularity of tasks, function calls, system calls, or basic blocks. These approaches neither consider the entire software execution path nor provide a quantitative estimate of the presence of malware. This article presents a novel model for specifying the normal timing for execution paths in software applications using cumulative distribution functions of timing data in sliding execution windows. A probabilistic formulation is used to estimate the presence of malware for individual operations and sequences of operations within the paths. Operation and path-based thresholds are determined during the training process to minimize false positives. Finally, the article presents an optimization method to assist system developers in selecting which operations to monitor based on different optimization goals and constraints. Experimental results with a smart connected pacemaker, an unmanned aerial vehicle, and seven sophisticated mimicry malware implemented at different levels demonstrate the effectiveness of the proposed approach.

Procedure model for the development and launch of intelligent assistance systems

Abstract The paper analyses the current state of knowledge on approaches for the practical implementation of machine learning based assistance systems for production planning and control. A concept of a procedure model for application-oriented projects in the field of industrial series production is proposed. It focusses on order sequencing and machine allocation in a real time production environment. As part of an application-oriented research project, a use case is referenced. In this paper, a first conceptual approach is presented, using the example of an industrial production of printed circuit boards. In the following steps, practical suitability is checked on the basis of the practical reference, conclusions are drawn and the methodology will be developed further. The aim is a generally valid procedure model for industrial series production.

The Application of Digital Worker Assistance Systems to Support Workers with Disabilities in Assembly Processes

When looking at the last few decades, industrial manufacturing has undergone a continuous change. While years ago, the operator was not given much attention, now in the fourth industrial revolution (also Industry 4.0) the worker himself comes to the fore. An additional aspect through which the worker emerges in the context of industry is the ever-increasing shortage of skilled workers together with the demographic change. This leads to a progress in the development of worker assistance systems for supporting the operator during his/her daily work. Looking precisely at the workers in today’s manufacturing production reveals a large variety of different user groups with diverse needs. This paper focuses specifically on one user group, namely people with disabilities. The study was conducted together with a social enterprise in which people with mental/physical disabilities do mostly assembly work for other companies. Necessary capabilities for workers in general were collected through interviews with stakeholder associations, companies, and employees. Together with the supervising employees of a social enterprise, the necessary capabilities for the workers with disabilities were identified and compared with the necessary ones to conduct the assembly process. Subsequently, experiments were made with a digital worker assistance system to investigate the effectiveness of this system. The results show an overall positive acceptance of the target group towards assistance systems and an increase in process accuracy and process success.

Development and Evaluation of a Mixed Reality Assistance System in the Context of Manual Assembly

Manufacturing environments face the challenge of ever-increasing complexity in their products due to the continuously increasing degree of individuality. Together with the demand for meeting high quality standards, this leads to the requirement to assist workers during a manufacturing process. Cognitive assistance systems which provide work-related information during their work tasks have been proven to be a successful aid for these challenges. Whenever a new emerging technology is applied for worker assistance, the technology should be tested to evaluate its positive or negative impact in a specified assembly process. This work aims to test the impact of an emerging technology, the Microsoft HoloLens 2 Head Mounted Display (HMD), in a manufacturing environment. Therefore, a prototypical Mixed Reality (MR) application is introduced which provides assembly-related information to support the worker during the work process and to ensure a zero failure culture. To evaluate the impact of the presented application a comparative study is conducted, comparing existing text-based instructions on touchscreens to the newly-integrated interactive 3D assembly instructions. Although an efficiency can not be shown in the presented study, a significant increase in quality of the assembled product was gained, leading the way into a zero failure culture.

On-Site Learning Factories for Implementing Artificial Intelligence Systems in Production

When introducing a disruptive technology such as artificial intelligence (AI) into a company, it is important to involve the employees from the beginning and to inform them comprehensively about the opportunities and risks of the technology. Traditional means of communication such as information brochures, company meetings and email circulars are not suitable for this purpose, as they only allow limited feedback and often present the workforce with a fait accompli. The KI_Cafe method presents a new kind of introduction strategy that follows an on-site learning factory concept. This includes information and discussion opportunities for all participants based on example scenarios, as well as the development and testing of a specific AI system. For this purpose, a requirements definition, system development and practical implementation of an AI-based assistance system are combined with recurring company-public experimental spaces, the KI_Cafe Learning factory. In a use case, a novel-based system for assessing the cutting edge quality of milling tools is developed and implemented for two small and medium sized enterprises. This enables an independent assess the condition of a milling tool and thus contributes to relieving the workload of the integrated tool pre-setting. During the development, all organizational levels from the machine operators to the managers will be included in order to map the individual needs of the device and to create an understanding of the AI system at an early stage. Accompanying this, various exhibits on the topic of AI and the current development status of the new measuring system will accessible in the KI_Cafe.

Overview of Embedded Systems to Build Reliable and Safe ADAS and AD Systems

Automotive industry is a key sector in developed countries, taking advantage from Electronic and Semiconductor industries, for which this work is focused on, including an overview of embedded systems and related technologies for Advanced Driver Assistance Systems (ADAS) development, end user applications and their implementation (SoCs, Application Processors-APs, MCUs, software and boards), manufacturers solutions, architectures, trends and other aspects (like methodologies) to improve functional safety, reliability and performances. The current status to permit the transition from ADAS to Autonomous Driving (AD) systems and Self-Driving Cars (SDC) is also explored.

Human-Automation - Railway remote control: how to define shared information and functions?

As it had already been observed in other domains, such as aircraft or automotive sectors, the development of fully automated driving systems in the mainline railway sector faces multiple technical and safety barriers. Before such an advanced system can be overcome, systems with intermediate and adaptive levels of automation must be considered and studied. In this context, human operators maintain a crucial role for the train driving activity and their interactions with technical systems and other agents must remain among the main focuses of the conception phase. These interactions are considered through a thorough Human-Machine cooperation model. The objective of this paper is to present a conception and evaluation method implementing this model in the development of assistance systems for cooperation with human operators. The method is currently being applied for train remote driving as part of the TC-Rail project, where the aim of the study is to focus on Human-Factors aspects. A second phase of development is currently in progress and has been complemented by the results of a first phase. Along with the conception method, the state of this second phase, as well as use cases for future tests that implement it, are presented in this paper.

Identifying Big Five Personality Traits through Controller Area Network Bus Data

As adapting vehicles to drivers’ preferences has become an important focus point in the automotive sector, a more convenient, objective, real-time method for identifying drivers’ personality traits is increasingly important. Only recently has increased availability of driving signals obtained via controller area network (CAN) bus provided new perspectives for investigating personality differences. This study proposes a new methodology for identifying drivers’ Big Five personality traits through driving signals, specifically accelerator pedal angle, frontal acceleration, steering wheel angle, lateral acceleration, and speed. Data were collected from 92 participants who were asked to drive a car along a pre-defined 15 km route. Using statistical methods and the discrete Fourier transform, some time-frequency features related to driving were extracted to establish models for identifying participants’ Big Five personality traits. For these five personality trait dimensions, the coefficients of determination of effective predictive models were between 0.19 and 0.74, the root mean squared errors were between 2.47 and 4.23, and the correlations between predicted scores and self-reported questionnaire scores were considered medium to strong (0.56–0.88). The results showed that personality traits can be revealed through driving signals, and time-frequency features extracted from driving signals are effective in characterizing and identifying Big Five personality traits. This approach could be of potential value in the development of in-car integration or driver assistance systems and indicates a possible direction for further research on convenient psychometric methods.

Fuse -Replacement Noise-Filter Development for Automotive Audio–Video Navigation Systems Based on Loudness Isotropy Curves

With the development of advanced driver assistance systems, the more reliable the autonomous driving technology is, the more the rest and entertainment times of the driver of the car increases. Hence, the importance of the entertainment function of automotive audio-video navigation (AVN) systems is increasing. Currently, the AVN system of automobiles has a monitoring function for fault diagnosis and a combination of functions. Applying these technologies is challenging for drivers who want to tune the audio quality to their musical taste. In this study, a method for upgrading the sound quality using a power supply noise filter without deforming the AVN system was developed. The low-pass attenuation that appeared as a side effect was solved by applying a filter using the loudness isotropic curve. In the installation method of the filter, the method of using a fuse holder minimized the inconvenience of AVN detachment and wiring. Based on the results obtained in this study, further research and improvement of the filter are required for audio tuning of various models.

Performance Evaluation of Different Grade IMUs for Diagnosis Applications in Land Vehicular Multi-Sensor Architectures

Vehicular positioning systems are necessary for the development of autonomous vehicles and advanced driver assistance systems (ADAS). In recent years, Inertial Measurement Units (IMU) based on micro-electromechanical systems (MEMS) have been included in proposals for multi-sensor positioning system architectures in order to take advantage of their cost and size. The measurement errors propagation to the positioning solution have limited its application for long-term positioning solution. However, it can play a key role in those applications where tri-axial attitude and accelerations can be indicators of curves, slopes, cants, etc. and it can be used as diagnostic of other sensors measurements. This work is focused on the use of IMUs for diagnostic applications and it compares medium-end (xSens MTi-100) and low-end (Bosch BMI160) grade MEMS-based IMUs in an experimental road test using a fusion of a high-end IMU (KVH GEO-FOG), GNSS and wheel speed sensor as reference. In addition, a research question about whether the calibration is the main reason between different grade IMUs has been formulated. Thus, a simple, manufacturable and cost-efficient calibration technique is applied to the low-end IMU in order to compare its performance improvement with the medium-end one. The raw measurements (angular rates and specific forces) and navigation states (tri-axial attitude and accelerations) are considered for diagnosis and they are statistically compared to evaluate the performance of each IMU. It is concluded that the calibration technique used makes the low-end IMU performance similar to that of the medium-end one. Consequently, this work contributes to optimizing the cost of land vehicular positioning systems when choosing the most appropriate sensor based on the accuracy and precision required for the application.

What did the Administrative Reform of 1999 Change? On the periodization of social assistance system development 1990–2020

The aim of this paper is to propose a periodization of the Polish welfare assistance system in the Third Polish Republic. Focusing on the organizational structure, the development of the welfare assistance system in the last thirty years has been described. Based on this, two stages of social assistance development are described. Stage 1 (1990–1999), was characterized by the organization of welfare assistance system in the form of a poorly developed vertical system. Stage 2 (1999–2020), is characterized by the organization of the welfare assistance system in the form of an extensive horizontal system.