Human-Machine Cooperative Decision Making

Diese Dissertation beschäftigt sich mit der gemeinsamen Entscheidungsfindung in der Mensch-Maschine-Kooperation und liefert neue Erkenntnisse, welche von der theoretischen Modellierung bis zu experimentellen Untersuchungen reichen. Zunächst wird eine methodische Klassifikation bestehender Forschung zur Mensch-Maschine-Kooperation vorgenommen und der Forschungsfokus dieser Dissertation mithilfe eines vorgestellten Taxonomiemodells der Mensch-Maschine-Kooperation, dem Butterfly-Modell, abgegrenzt. Darauffolgend stellt die Dissertation zwei mathematische Verhaltensmodelle der gemeinsamen Entscheidungsfindung von Mensch und Maschine vor: das Adaptive Verhandlungsmodell und den n-stufigen War of Attrition. Beide modellieren den Einigungsprozess zweier emanzipierter Kooperationspartner und unterscheiden sich hinsichtlich ihrer Ursprünge, welche in der Verhandlungs- beziehungsweise Spieltheorie liegen. Zusätzlich wird eine Studie vorgestellt, die die Eignung der vorgeschlagenen mathematischen Modelle zur Beschreibung des menschlichen Nachgebeverhaltens in kooperativen Entscheidungsfindungs-Prozessen nachweist. Darauf aufbauend werden zwei modellbasierte Automationsdesigns bereitgestellt, welche die Entwicklung von Maschinen ermöglichen, die an einem Einigungsprozess mit einem Menschen teilnehmen können. Zuletzt werden zwei experimentelle Untersuchungen der vorgeschlagenen Automationsdesigns im Kontext von teleoperierten mobilen Robotern in Such- und Rettungsszenarien und anhand einer Anwendung in einem hochautomatisierten Fahrzeug präsentiert. Die experimentellen Ergebnisse liefern empirische Evidenz für die Überlegenheit der vorgestellten modellbasierten Automationsdesigns gegenüber den bisherigen Ansätzen in den Aspekten der objektiven kooperativen Performanz, des menschlichen Vertrauens in die Interaktion mit der Maschine und der Nutzerzufriedenheit. So zeigt diese Dissertation, dass Menschen eine emanzipierte Interaktion mit Bezug auf die Entscheidungsfindung bevorzugen, und leistet einen wertvollen Beitrag zur vollumfänglichen Betrachtung und Verwirklichung von Mensch-Maschine-Kooperationen

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)

A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Enabling Machine Understandable Exchange of Energy Consumption Information in Intelligent Domotic Environments

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontolog

17 Human-Car confluence: “Socially-Inspired driving mechanisms”

With self-driving vehicles announced for the 2020s, today’s challenges in Intelligent Transportation Systems (ITS) lie in problems related to negotiation and decision making in (spontaneously formed) car collectives. Due to the close coupling and interconnectedness of the involved driver-vehicle entities, effects on the local level induced by cognitive capacities, behavioral patterns, and the social context of drivers, would directly cause changes on the macro scale. To illustrate, a driver’s fatigue or emotion can influence a local driver-vehicle feedback loop, which is directly translated into his or her driving style, and, in turn, can affect driving styles of all nearby drivers. These transitional, yet collective driver state and driving style changes raise global traffic phenomena like jams, collective aggressiveness, etc. To allow harmonic coexistence of autonomous and self-driven vehicles, we investigate in this chapter the effects of socially-inspired driving and discuss the potential and beneficial effects its application should have on collective traffic