Ethical Issues in the New Digital Era: The Case of Assisting Driving

Mobility is associated with driving a vehicle. Age-related declines in the abilities of older persons present certain obstacles to safe driving. The negative effects of driving cessation on older adults’ physical, mental, cognitive, and social functioning are well reported. Automated driving solutions represent a potential solution to promoting driver persistence and the management of fitness to drive issues in older adults. Technology innovation influences societal values and raises ethical questions. The advancement of new driving solutions raises overarching questions in relation to the values of society and how we design technology (a) to promote positive values around ageing, (b) to enhance ageing experience, (c) to protect human rights, (d) to ensure human benefit and (e) to prioritise human well-being. To this end, this chapter reviews the relevant ethical considerations in relation to assisted driving solutions. Further, it presents a new ethically aligned system concept for assisted driving. It is argued that human benefit, well-being and respect for human identity and rights are important goals for new automated driving technologies. Enabling driver persistence is an issue for all of society and not just older adult

Driver Acceptance of Advanced Driver Assistance Systems and Semi-Autonomous Driving Systems

Advanced Driver Assistance Systems (ADAS) and semi-autonomous driving systems are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., are well accepted and endorsed by transportation safety researchers and federal transportation agencies. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of ADAS and semi-autonomous driving systems into the transportation system. These technologies will not achieve their potential if drivers do not accept them and use them in a sustainable and appropriate manner. The potential benefits of these in-vehicle assistive systems presents a strong need for research. A comprehensive review of current literature on the definitions of acceptance, acceptance modelling approaches, and assessment techniques was carried out to explore and summarize the different approaches adopted by previous researchers. The review identified three major research needs: a comprehensive evaluation of general technology acceptance models in the context of ADAS, development of an acceptance model specifically for ADAS and similar technologies, and development of an acceptance assessment questionnaire. Two studies were conducted to address these needs. In the first study, data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants were exposed to an ADAS and, based on their experience, responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, reliability, etc. The results of the first study showed the utility of the general technology acceptance theories to model driver acceptance. A Unified Model of Driver Acceptance (UMDA) and two versions (a long version with 21 items and a short version with 13 items) of an acceptance assessment questionnaire were also developed, based on the results of the first study. The second was conducted to validate the findings of first study. The results of the second study found statistical evidence validating UMDA and the two versions of the acceptance assessment questionnaire

Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behavior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver

Shared control strategies for automated vehicles

188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada

An enactive approach to perceptual augmentation in mobility

Event predictions are an important constituent of situation awareness, which is a key objective for many applications in human-machine interaction, in particular in driver assistance. This work focuses on facilitating event predictions in dynamic environments. Its primary contributions are 1) the theoretical development of an approach for enabling people to expand their sampling and understanding of spatiotemporal information, 2) the introduction of exemplary systems that are guided by this approach, 3) the empirical investigation of effects functional prototypes of these systems have on human behavior and safety in a range of simulated road traffic scenarios, and 4) a connection of the investigated approach to work on cooperative human-machine systems. More specific contents of this work are summarized as follows: The first part introduces several challenges for the formation of situation awareness as a requirement for safe traffic participation. It reviews existing work on these challenges in the domain of driver assistance, resulting in an identification of the need to better inform drivers about dynamically changing aspects of a scene, including event probabilities, spatial and temporal distances, as well as a suggestion to expand the scope of assistance systems to start informing drivers about relevant scene elements at an early stage. Novel forms of assistance can be guided by different fundamental approaches that target either replacement, distribution, or augmentation of driver competencies. A subsequent differentiation of these approaches concludes that an augmentation-guided paradigm, characterized by an integration of machine capabilities into human feedback loops, can be advantageous for tasks that rely on active user engagement, the preservation of awareness and competence, and the minimization of complexity in human- machine interaction. Consequently, findings and theories about human sensorimotor processes are connected to develop an enactive approach that is consistent with an augmentation perspective on human-machine interaction. The approach is characterized by enabling drivers to exercise new sensorimotor processes through which safety-relevant spatiotemporal information may be sampled. In the second part of this work, a concept and functional prototype for augmenting the perception of traffic dynamics is introduced as a first example for applying principles of this enactive approach. As a loose expression of functional biomimicry, the prototype utilizes a tactile inter- face that communicates temporal distances to potential hazards continuously through stimulus intensity. In a driving simulator study, participants quickly gained an intuitive understanding of the assistance without instructions and demonstrated higher driving safety in safety-critical highway scenarios. But this study also raised new questions such as whether benefits are due to a continuous time-intensity encoding and whether utility generalizes to intersection scenarios or highway driving with low criticality events. Effects of an expanded assistance prototype with lane-independent risk assessment and an option for binary signaling were thus investigated in a separate driving simulator study. Subjective responses confirmed quick signal understanding and a perception of spatial and temporal stimulus characteristics. Surprisingly, even for a binary assistance variant with a constant intensity level, participants reported perceiving a danger-dependent variation in stimulus intensity. They further felt supported by the system in the driving task, especially in difficult situations. But in contrast to the first study, this support was not expressed by changes in driving safety, suggesting that perceptual demands of the low criticality scenarios could be satisfied by existing driver capabilities. But what happens if such basic capabilities are impaired, e.g., due to poor visibility conditions or other situations that introduce perceptual uncertainty? In a third driving simulator study, the driver assistance was employed specifically in such ambiguous situations and produced substantial safety advantages over unassisted driving. Additionally, an assistance variant that adds an encoding of spatial uncertainty was investigated in these scenarios. Participants had no difficulties to understand and utilize this added signal dimension to improve safety. Despite being inherently less informative than spatially precise signals, users rated uncertainty-encoding signals as equally useful and satisfying. This appreciation for transparency of variable assistance reliability is a promising indicator for the feasibility of an adaptive trust calibration in human-machine interaction and marks one step towards a closer integration of driver and vehicle capabilities. A complementary step on the driver side would be to increase transparency about the driver’s mental states and thus allow for mutual adaptation. The final part of this work discusses how such prerequisites of cooperation may be achieved by monitoring mental state correlates observable in human behavior, especially in eye movements. Furthermore, the outlook for an addition of cooperative features also raises new questions about the bounds of identity as well as practical consequences of human-machine systems in which co-adapting agents may exercise sensorimotor processes through one another.Die Vorhersage von Ereignissen ist ein Bestandteil des Situationsbewusstseins, dessen Unterstützung ein wesentliches Ziel diverser Anwendungen im Bereich Mensch-Maschine Interaktion ist, insbesondere in der Fahrerassistenz. Diese Arbeit zeigt Möglichkeiten auf, Menschen bei Vorhersagen in dynamischen Situationen im Straßenverkehr zu unterstützen. Zentrale Beiträge der Arbeit sind 1) eine theoretische Auseinandersetzung mit der Aufgabe, die menschliche Wahrnehmung und das Verständnis von raum-zeitlichen Informationen im Straßenverkehr zu erweitern, 2) die Einführung beispielhafter Systeme, die aus dieser Betrachtung hervorgehen, 3) die empirische Untersuchung der Auswirkungen dieser Systeme auf das Nutzerverhalten und die Fahrsicherheit in simulierten Verkehrssituationen und 4) die Verknüpfung der untersuchten Ansätze mit Arbeiten an kooperativen Mensch-Maschine Systemen. Die Arbeit ist in drei Teile gegliedert: Der erste Teil stellt einige Herausforderungen bei der Bildung von Situationsbewusstsein vor, welches für die sichere Teilnahme am Straßenverkehr notwendig ist. Aus einem Vergleich dieses Überblicks mit früheren Arbeiten zeigt sich, dass eine Notwendigkeit besteht, Fahrer besser über dynamische Aspekte von Fahrsituationen zu informieren. Dies umfasst unter anderem Ereigniswahrscheinlichkeiten, räumliche und zeitliche Distanzen, sowie eine frühere Signalisierung relevanter Elemente in der Umgebung. Neue Formen der Assistenz können sich an verschiedenen grundlegenden Ansätzen der Mensch-Maschine Interaktion orientieren, die entweder auf einen Ersatz, eine Verteilung oder eine Erweiterung von Fahrerkompetenzen abzielen. Die Differenzierung dieser Ansätze legt den Schluss nahe, dass ein von Kompetenzerweiterung geleiteter Ansatz für die Bewältigung jener Aufgaben von Vorteil ist, bei denen aktiver Nutzereinsatz, die Erhaltung bestehender Kompetenzen und Situationsbewusstsein gefordert sind. Im Anschluss werden Erkenntnisse und Theorien über menschliche sensomotorische Prozesse verknüpft, um einen enaktiven Ansatz der Mensch-Maschine Interaktion zu entwickeln, der einer erweiterungsgeleiteten Perspektive Rechnung trägt. Dieser Ansatz soll es Fahrern ermöglichen, sicherheitsrelevante raum-zeitliche Informationen über neue sensomotorische Prozesse zu erfassen. Im zweiten Teil der Arbeit wird ein Konzept und funktioneller Prototyp zur Erweiterung der Wahrnehmung von Verkehrsdynamik als ein erstes Beispiel zur Anwendung der Prinzipien dieses enaktiven Ansatzes vorgestellt. Dieser Prototyp nutzt vibrotaktile Aktuatoren zur Kommunikation von Richtungen und zeitlichen Distanzen zu möglichen Gefahrenquellen über die Aktuatorposition und -intensität. Teilnehmer einer Fahrsimulationsstudie waren in der Lage, in kurzer Zeit ein intuitives Verständnis dieser Assistenz zu entwickeln, ohne vorher über die Funktionalität unterrichtet worden zu sein. Sie zeigten zudem ein erhöhtes Maß an Fahrsicherheit in kritischen Verkehrssituationen. Doch diese Studie wirft auch neue Fragen auf, beispielsweise, ob der Sicherheitsgewinn auf kontinuierliche Distanzkodierung zurückzuführen ist und ob ein Nutzen auch in weiteren Szenarien vorliegen würde, etwa bei Kreuzungen und weniger kritischem longitudinalen Verkehr. Um diesen Fragen nachzugehen, wurden Effekte eines erweiterten Prototypen mit spurunabhängiger Kollisionsprädiktion, sowie einer Option zur binären Kommunikation möglicher Kollisionsrichtungen in einer weiteren Fahrsimulatorstudie untersucht. Auch in dieser Studie bestätigen die subjektiven Bewertungen ein schnelles Verständnis der Signale und eine Wahrnehmung räumlicher und zeitlicher Signalkomponenten. Überraschenderweise berichteten Teilnehmer größtenteils auch nach der Nutzung einer binären Assistenzvariante, dass sie eine gefahrabhängige Variation in der Intensität von taktilen Stimuli wahrgenommen hätten. Die Teilnehmer fühlten sich mit beiden Varianten in der Fahraufgabe unterstützt, besonders in Situationen, die von ihnen als kritisch eingeschätzt wurden. Im Gegensatz zur ersten Studie hat sich diese gefühlte Unterstützung nur geringfügig in einer messbaren Sicherheitsveränderung widergespiegelt. Dieses Ergebnis deutet darauf hin, dass die Wahrnehmungsanforderungen der Szenarien mit geringer Kritikalität mit den vorhandenen Fahrerkapazitäten erfüllt werden konnten. Doch was passiert, wenn diese Fähigkeiten eingeschränkt werden, beispielsweise durch schlechte Sichtbedingungen oder Situationen mit erhöhter Ambiguität? In einer dritten Fahrsimulatorstudie wurde das Assistenzsystem in speziell solchen Situationen eingesetzt, was zu substantiellen Sicherheitsvorteilen gegenüber unassistiertem Fahren geführt hat. Zusätzlich zu der vorher eingeführten Form wurde eine neue Variante des Prototyps untersucht, welche räumliche Unsicherheiten der Fahrzeugwahrnehmung in taktilen Signalen kodiert. Studienteilnehmer hatten keine Schwierigkeiten, diese zusätzliche Signaldimension zu verstehen und die Information zur Verbesserung der Fahrsicherheit zu nutzen. Obwohl sie inherent weniger informativ sind als räumlich präzise Signale, bewerteten die Teilnehmer die Signale, die die Unsicherheit übermitteln, als ebenso nützlich und zufriedenstellend. Solch eine Wertschätzung für die Transparenz variabler Informationsreliabilität ist ein vielversprechendes Indiz für die Möglichkeit einer adaptiven Vertrauenskalibrierung in der Mensch-Maschine Interaktion. Dies ist ein Schritt hin zur einer engeren Integration der Fähigkeiten von Fahrer und Fahrzeug. Ein komplementärer Schritt wäre eine Erweiterung der Transparenz mentaler Zustände des Fahrers, wodurch eine wechselseitige Anpassung von Mensch und Maschine möglich wäre. Der letzte Teil dieser Arbeit diskutiert, wie diese Transparenz und weitere Voraussetzungen von Mensch-Maschine Kooperation erfüllt werden könnten, indem etwa Korrelate mentaler Zustände, insbesondere über das Blickverhalten, überwacht werden. Des Weiteren ergeben sich mit Blick auf zusätzliche kooperative Fähigkeiten neue Fragen über die Definition von Identität, sowie über die praktischen Konsequenzen von Mensch-Maschine Systemen, in denen ko-adaptive Agenten sensomotorische Prozesse vermittels einander ausüben können

A Real-Time Predictive Vehicular Collision Avoidance System on an Embedded General-Purpose GPU

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Towards Safe Autonomous Driving

Autonomous driving is expected to bring several benefits, in particular regarding safety. This thesis aim to contribute towards two questions concerning safety: "What is the potential safety benefit of autonomous driving?\u27\u27 and "How can we ensure safe operation of such vehicles?\u27\u27.In the first part of the thesis, methods for evaluating the safety benefit are investigated. In particular predictive effectiveness evaluation based on resimulation of accident data, using models to estimate new outcomes in case the safety system had been available. To illustrate the methodology, four examples of gradual increase in model complexity are presented. First, an Autonomous Emergency Braking (AEB) system using a sensor model, decision algorithm, vehicle dynamics model and regression based injury model. This is extended in a Forward Collision Warning (FCW) system which additionally requires a driver model to simulate driver reactions. The third example shows how an active, AEB, and passive, airbag, system can be combined.\ua0Finally the fourth example combines several systems to emulate a highly automated vehicle. Apart from predicting the real world performance, this analysis also identifies current safety gaps by studying the residual of the accident set.Safety benefit estimation using accident data gives an evaluation on the current accident distributions, however, the systems may introduce new accidents if not operated as intended. In the second part of the thesis, safety verification processes with the intent of preventing unsafe operation, are presented. This is particularly challenging for machine learning based components, such as neural networks. In this case, traditional analytical verification approaches are\ua0difficult to apply due to the non-linearity and high dimensional parameter spaces. Similarly, statistical safety arguments often require unfeasible amounts of annotated validation data. Instead, monitor functions are investigated as a complement to increase safety during operation. The method presented estimates the similarity of the driving environment, compared to the training data, where decisions inferred from novel data can be considered less reliable.\ua0Although not providing a complete safety assurance, the methodology show promising initial results for increasing safety. In addition, it could potentially be used to collect novel data and reduce redundancy in training data