<div>Due to recent advances in sensor technology and the exponential increase in computation power of electronic control units (ECUs) along with their increasing affordability, active safety and vehicle automation have become major trends in the commercial vehicle industry. New regulations for increased safety are also a major driver behind the industry's increased interest in that topic. As a result, being a crucial part of vehicle automation, steering systems had to be adapted to enable Active Steering. Consequently, commercial vehicle steering designers introduced the concept of torque and angle overlay using an electric motor in series with the conventional hydraulic steering system. However, despite the fact that these systems are becoming more prevalent in the market, they still suffer from inefficiencies intrinsic to the conventional hydraulic steering system still being used. These inefficiencies are a result of</div><div>flow metering losses due to the use of control valves to regulate the pump flow output, as well as inside the steering gear with the use control valves to build assistance pressure.</div><div><br></div><div><div>In this research project, we investigate the potential use of the proven pump Displacement Control (DC) technology in steering on-highway commercial vehicles. DC pumps have been shown to signicantly improve system efficiency as they allow the removal of control valves typically used to regulate </div><div>ow [1]. Instead, the displacement of the pump can be directly controlled to vary the pump's flow rate and direction,</div><div>and thus eliminating throttling losses. The DC technology has been successfully used in a steer-by-wire conguration for an articulated frame steering vehicle and has been shown to signicantly improve efficiency and productivity, as well as result in a reduction in fuel consumption [2].</div></div><div><br></div><div><div>In this work, we propose a steer-by-wire system, using DC pump technology, for on-highway commercial vehicles, and present the dierent possible congurations in which it can be implemented. Moreover, the benets and drawbacks of the steer-by-wire system are researched and identied. Subsequently, the system is designed and validated in simulation, on laboratory test setup, as well as on a test vehicle to prove its feasibility.</div></div><div><br></div><div><div>Chief among the drawbacks of the steer-by-wire system is potential failures that can lead to the complete loss of the steering function of the vehicle. As a result, different possible fail-safe mechanisms are researched from which the most suitable ones are proposed to allow the steer-by-wire system to fail safely. Moreover, two of the proposed fail-safe mechanism are implemented onto the test vehicle to prove and validate their feasibility.</div></div><div><br></div><div><div>Furthermore, an alternative way of using displacement controlled pumps for active steering is be proposed. For this concept, we investigate the possibility of actively controlling the driver's steering effort by varying the pump displacement while maintaining the mechanical link between the steering wheel and the road wheels. If successful, this method will allow for a more efficient way of providing steering assistance as it does away with the conventional control valves used to build pressure and regulate pump flow, and thus eliminating throttling losses. This method has also the advantage of having an intrinsic fail-safe mechanism with manual steering being always possible should the hydraulic or electric systems fail.</div></div>

Driving comfort is considered a key factor for broad public acceptance of automated driving. Based on continuous driver/passenger monitoring, potential discomfort could be avoided by adapting automation features such as the driving style. The EU-project MEDIATOR (mediatorproject.eu) aims at developing a mediating system in automated vehicles by constantly evaluating the performance of driver and automation. As facial expressions could be an indicator of discomfort, a driving simulator study has been carried out to investigate this relationship. A total of 41 participants experienced three potentially uncomfortable automated approach situations to a truck driving ahead. The face video of four cameras was analyzed with the Visage facial feature detection and face analysis software, extracting 23 Action Units (AUs). Situation-specific effects showed that the eyes were kept open and eye blinks were reduced (AU43). Inner brows (AU1) as well as upper lids (AU5) raised, indicating surprise. Lips were pressed (AU24) and stretched (AU20) as sign for tension. Overall, facial expression analysis could contribute to detect discomfort in automated driving.

Das autonome Fahren ist derzeit aufgrund zahlreicher aktueller Forschungs- und Entwicklungsprojekte namhafter Automobilhersteller und -zulieferer im Fokus des öffentlichen Interesses. Der stetige Fortschritt des autonomen Fahrens kann unter anderem auf der jährlich in Las Vegas stattfindenden Consumer Electronics Show (CES) festgestellt werden, welche seit einiger Zeit auch von Automobilherstellern als Plattform zur Vorstellung neuer Technologien genutzt wird. So demonstrierte die Audi AG einen A7, der vollständig autonom vom Silicon Valley eine Strecke von 900 km Länge nach Las Vegas fuhr. Darüber hinaus legen auch automobilfremde Hochtechnologiekonzerne ihr Interesse an dieser Schlüsseltechnologie offen und präsentieren eigene Forschungs- und Entwicklungsarbeiten. Google verkündete die Forschung an einem eigenen autonomen Fahrzeug und auch Spekulationen über das Interesse von Apple wurden medial publik. Dennoch stehen die rasante Entwicklungsgeschwindigkeit und die öffentlich bereits präsentierte Funktionsfähigkeit des Autonomen Fahrens im Kontrast zu der geringen Anwendung im deutschen Straßenverkehr. In Deutschland und anderen Ländern sind bisher nur Pilotprojekte aus Forschung und Entwicklung existent. Diese unterliegen aktuell noch vielen Restriktionen. Dies macht deutlich, dass trotz der bisher erreichten Einzelerfolge dieser Technologie keine Serienreife besteht. So müssen für den tatsächlichen Einsatz des Autonomen Fahrens im Straßenverkehr technologische, soziologische sowie gesetzliche Rahmenbedingungen eingehalten, erweitert, angepasst, respektive erst noch definiert wer-den. Insbesondere im urbanen Umfeld besteht hier hoher Forschungsbedarf auch im Hin-blick auf technologische Rahmenbedingungen. Die vorliegende Arbeit soll einen Einblick über offene Fragestellungen und technologische Hürden sowie deren Bedeutung für das Autonome Fahren ermöglichen. Zudem wird ein kurzer Überblick darüber gegeben, wie dies auf den Sektor der Baumaschinen übertragbar ist.:1. Autonomes Fahren im Automobil 1.1. Stufen der Automation nach SAE-Standard J3016 1.2. Einführungsstrategien für autonome Fahrfunktionen 2. Rahmenbedingungen des Autonomen Fahrens 2.1. technologische Rahmenbedingungen 2.1.1. Umfeldwahrnehmung 2.1.2. Kooperation der Verkehrsteilnehmer 2.1.3. Hochgenaue Karten und Lokalisierung 2.1.4. Herausforderung für die Absicherung und Systemarchitektur 2.2. Soziologische Rahmenbedingungen 2.2.1. Vertrauen des Fahrers in die Technik und Fahrerüberwachung 2.2.2. Dilemmasituationen 2.3. Gesetzliche und rechtliche Rahmenbedingungen 2.3.1. ECE-R79 und Wiener Übereinkommen 2.3.2. Datenschutz und Datensicherheit 2.3.3. Haftung 3. Übertragbarkeit auf Baumaschinen 3.1. Aktuelle Entwicklungen und Beispiele 3.2. Bezug zum Automobil 3.3. Use-Case Straßenbau/Asphaltbau 3.4. Übertragbarkeit von Rahmenbedingungen 4. Fazit Quellenverzeichnis

New cooperative intelligent transportation system (C-ITS) applications become enabled thanks to advances in communication technologies between vehicles(V2V) and with the infrastructure (V2I). Communicating vehicles share information with each other and cooperate, which results in improved safety, fuel economy, and traffic efficiency. An example of a C-ITS application is platooning, which comprises a string of vehicles that travel together with short inter-vehicle distances (IVDs). Any solution related to C-ITS must comply with high safety requirements in order to pass standardization and be commercially deployed. Furthermore, trusted safety levels should be assured even for critical scenarios. This thesis studies the conditions that guarantee safety in emergency braking scenarios for heterogeneous platooning, or string-like, formations of vehicles. In such scenarios, the vehicle at the head of the string emergency brakes and all following vehicles have to automatically react in time to avoid rear-end collisions. The reaction time can be significantly decreased with vehicle-to-vehicle (V2V) communication usage since the leader can explicitly inform other platooning members about the critical braking. The safety analysis conducted in the thesis yields computationally efficient methods and algorithms for calculating minimum inter-vehicle distances that allow avoiding rear-end collisions with a predefined high guarantee. These IVDs are theoretically obtained for an open-loop and a closed-loop configurations. The former implies that follower drives with a constant velocity until braking starts, whereas in the latter, an adaptive cruise control (ACC) with a constant-distance policy serves as a controller. In addition, further optimization of inter-vehicle distances in the platoon is carried out under an assumption of centralized control. Such an approach allows achieving better fuel consumption and road utilization. The performed analytical comparison suggests that our proposed V2V communication based solution is superior to classical automated systems, such as automatic emergency braking system (AEBS), which utilizes only onboard sensors and no communication. Wireless communication, enabling to know the intentions of other vehicles almost immediately, allows for smaller IVDs whilst guaranteeing the same level of safety. Overall, the presented thesis highlights the importance of C-ITS and, specifically, V2V in the prevention of rear-end collisions in emergency scenarios. Future work directions include an extension of the obtained results by considering more advanced models of vehicles, environment, and communication settings; and applying the proposed algorithms of safety guaranteeing to other controllers, such as ACC with a constant time headway policy.

Understandings of road traffic interactions are largely based on human-human interactions. However, the development of vehicles controlled by highly auto- mated driving systems (ADS) would introduce a radically novel type of road user. This compilation thesis explores encounters between these “autonomous vehicles” (AVs) and human vulnerable road users (VRUs) such as pedestrians and cyclists. The included publications are connected to three research questions. First, empirical studies are reviewed to highlight existing interactive be- haviors and communication cues. This is followed by a methodological question of how to investigate AV-VRU interactions. Finally, VRUs’ experiences from initial experiments on AV crossing encounters are presented. While road user trajectories and kinematic behaviors are viewed as primary mechanisms to facilitate traffic interactions, they might also be influenced by cues such as appearances, gestures, eye-gaze, and external human-machine interfaces (eHMI). Using the Wizard-of-Oz approach, we are able to explore VRU encounters with a seemingly highly automated vehicle. Compared to meeting an attentive driver, AV encounters resulted in a reported lower willingness to cross, lower perceived safety, and less calm emotional state, indicating that the absence of driver-centric cues could lead to interaction issues and impede acceptance of AVs. To further explore this, we included light-based eHMI to signal the driving mode and intent of the vehicle (e.g., intent to yield). Future research should continue to investigate how AVs may co-exist with human road users focusing on aspects such as behavioral adaptations, research methodologies, and the role of various eHMI.

Connected and automated vebicles (CA Vs) will shape traffic patterns in the future and greatly influence urban mobility. A particular challenge for CAVs is to anticipate the movements of other road users. This applies especially to micromobility vehicles (bicycles, smaU electric vehicles), whose traffic behaviour is difficult to predict and shaped from individual behaviour. The increasing coexistence of CAVs and other, conventionally driven modes of transport thus has a growing impact as well as multiple consequences for urban structures and public space. The following fundamental trends will shape the way people live together in cities in the coming years: 1. increasing share of CAVs and micromobility, 2. renaissance ofthe mixed and liveable city, 3. changes in mobility behaviour and the appreciation of public space ( especially due to climate change and the Covid 19-pandemic), as weil as 4. technical upgrading of infrastructure. These parallel developments will lead to both conflicts and opportunities for cities.[from Introduction]

<p>Adults aged 65 years and older have become the fastest-growing age group worldwide and are known to face perceptual, cognitive, and physical challenges in later stages of life. Automation may help to support these various age-related declines. However, many current automated systems often suffer from design limitations and occasionally require human intervention. To date, there is little guidance on how to design human-machine interfaces (HMIs) to help a wide range of users, especially older adults, transition to manual control. Multimodal interfaces, which present information in the visual, auditory, and/or tactile sensory channels, may be one viable option to communicate roles in human-automation systems, but insufficient empirical evidence is available for this approach. Also, the aging process is not homogenous across individuals, and physical and cognitive factors may better indicate one’s aging trajectory. Yet, the benefits that such individual differences have on task performance in human-automation systems are not well understood. Thus, the purpose of this dissertation work was to examine the effects of 1) multimodal interfaces and 2) one particular non-chronological age factor, engagement in physical exercise, on transitioning from automated to manual control dynamic automated environments. Automated driving was used as the testbed. The work was completed in three phases. </p><p><br></p> <p>The vehicle takeover process involves 1) the perception of takeover requests (TORs), 2) action selection from possible maneuvers that can be performed in response to the TOR, and 3) the execution of selected actions. The first phase focused on differences in the detection of multimodal TORs between younger and older drivers during the initial phase of the vehicle takeover process. Participants were asked to notice and respond to uni-, bi- and trimodal combinations of visual, auditory, and tactile TORs. Dependent measures were brake response time and maximum brake force. Overall, bi- and trimodal warnings were associated with faster responses for both age groups across driving conditions, but was more pronounced for older adults. Also, engaging in physical exercise was found to be correlated with smaller maximum brake force. </p><p><br></p> <p>The second phase aimed to quantify the effects of age and physical exercise on takeover task performance as a function of modality type and lead time (i.e., the amount of time given to make decisions about which action to employ). However, due to COVID-19 restrictions, the study could not be completed, thus only pilot data was collected. Dependent measures included decision making time and maximum resulting jerk. Preliminary results indicated that older adults had a higher maximum resulting jerk compared to younger adults. However, the differences in decision-making time and maximum resulting jerk were narrower for the exercise group (compared to the non-exercise group) between the two age groups. </p><p><br></p> <p>Given COVID-19 restrictions, the objective of phase two shifted to focus on other (non-age-related) gaps in the multimodal literature. Specifically, the new phase examined the effects of signal direction, lead time, and modality on takeover performance. Dependent measures included pre-takeover metrics, e.g., takeover and information processing time, as well as a host of post-takeover variables, i.e., maximum resulting acceleration. Takeover requests with a tactile component were associated with the faster takeover and information processing times. The shorter lead time was correlated with poorer takeover quality.</p><p><br></p> <p>The third, and final, phase used knowledge from phases one and two to investigate the effectiveness of meaningful tactile signal patterns to improve takeover performance. Structured and graded tactile signal patterns were embedded into the vehicle’s seat pan and back. Dependent measures were response and information processing times, and maximum resulting acceleration. Overall, in only instructional signal group, meaningful tactile patterns (either in the seat back or seat pan) had worse takeover performance in terms of response time and maximum resulting acceleration compared to signals without patterns. Additionally, tactile information presented in the seat back was perceived as most useful and satisfying.</p><p><br></p> <p>Findings from this research can inform the development of next-generation HMIs that account for differences in various demographic factors, as well as advance our knowledge of the aging process. In addition, this work may contribute to improved safety across many complex domains that contain different types and forms of automation, such as aviation, manufacturing, and healthcare.</p>

<p dir="ltr">Automated Driving Systems (ADS) have evolved significantly over the past decade. With conditionally automated driving systems still requiring constant driver supervision and human intervention upon system request, a driver’s readiness to take over from an ADS has significant safety implications. Research suggests that drivers using ADS are more likely to engage in non-driving-related tasks (NDRTs), and this engagement can deteriorate takeover performance. However, different NDRTs can involve engagement of physical, visual and/or cognitive resources, which all can affect the takeover process in different ways. The potential interaction effects among these factors may be the cause of mixed empirical findings regarding the influence of NDRT engagement on takeover readiness and performance. Additionally, with more advanced ADS, takeover scenarios are likely to be less urgent. Yet, the ways in which drivers behave in response to a takeover request to intervene during such less urgent scenarios while engaged in NDRTs is still not well understood.</p><p dir="ltr">The purpose of this dissertation is to provide a better understanding of drivers’ response behavior during a conditionally automated vehicle takeover process by analyzing drivers’ motor, visual, and cognitive readiness in response to a takeover request (TOR). The work was completed in two phases. The first phase focused on the effects of pre-takeover visual engagement on takeover readiness in urgent situations. Two experiments were conducted as part of this first phase. Particularly, Study 1 investigated drivers’ post-TOR visual attention allocation and cognitive readiness after continuous visual NDRT engagement before a TOR. Study 2 examined drivers’ pre-TOR visual attention allocation and takeover performance both during and after voluntary engagement with visual NDRTs. The second phase used a non-urgent takeover scenario to investigate drivers’ takeover behavior and visual attention allocation when prioritizing the engagement of visual-manual NDRTs that differed in terms of cognitive engagement levels.</p><p dir="ltr">Study 1 required continuous visual attention in NDRTs and manipulated only the location of visual attention before an auditory TOR. Dependent measures included duration, location, and directness eye-tracking measures after the TOR, as well as freeze-probe cognitive readiness scores. Overall, delayed visual attention re-allocation in the driving scene, less dispersed gaze patterns, and worse perception and comprehension of road hazards were associated with off-road visual NDRT engagement. In addition, no significant benefit of enforcing on-road visual attention before the TOR, compared to the baseline condition without NDRT requirements, were found. These findings highlight the need to investigate the effects of more naturalistic NDRT engagement on takeover attention reallocation and takeover performance.</p><p dir="ltr">Study 2 complemented Study 1 by allowing voluntary switching of visual attention between the NDRT and the driving scene prior to the TOR, with the driving task being a priority. In addition, Study 2 investigated drivers’ takeover quality and understanding of the takeover scene using the appropriateness of their takeover decisions. Dependent measures were pre- and post-takeover eye-tracking measures, aligning to those used in Study 1, in addition to motor response measures, longitudinal and lateral vehicle control measures, and decisions made in response to a road obstacle. Overall, the driver’s post-TOR behaviors were not significantly affected by NDRT conditions, but visual NDRT-induced differences in gaze distribution were associated with the appropriateness of takeover decisions.</p><p dir="ltr">Finally, Study 3 used knowledge from prior studies to isolate the effects of different levels of cognitive engagement in real-world visual-manual NDRTs. The purpose was to investigate the effects of cognitive engagement on drivers’ visual attention allocation before and during the takeover, as well as on takeover performance in non-urgent takeover scenarios, where NDRT engagement was a priority. Dependent measures included eye-tracking measures, takeover response time, and vehicle control measures, used in prior studies. In summary, engagement in NDRTs with higher levels of cognitive engagement resulted in significant differences in pre-TOR visual attention allocation and less stable takeover maneuvers.</p><p dir="ltr">The findings from this work contribute to a better understanding of the effects of different components of NDRT engagement on takeover performance in conditionally automated driving systems. Ultimately, this work can contribute to improving the design of next-generation human-machine interfaces in surface transportation, including driver monitoring systems and in-vehicle displays, that promote safer human-automation integration in future ADS.</p>

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety.

Des voitures complètement automatisées pourraient circuler sur les routes dans les décennies à venir. Elles permettraient aux automobilistes d’être conduits dans leur véhicule par un système informatique. Une telle innovation pourrait engendrer une révolution qui affecterait le rôle du conducteur et ses activités pendant le trajet. Actuellement, ces véhicules ne sont pas encore accessibles au grand public, et il demeure difficile de prédire précisément quand cela se produira, et quelles seront leurs caractéristiques techniques finales. Dans ce contexte, un des objectifs de cette thèse a été d’étudier dans quelle mesure la conduite complètement automatisée sera acceptée. Bien que l’automobiliste soit conduit par son véhicule, il pourrait être amené à en reprendre le contrôle manuel dans différentes circonstances. En effet, cette manœuvre pourrait être effectuée en situation d’urgence ou de manière anticipée par le conducteur alors qu’il pourrait être engagé dans une autre activité que la conduite. La réalisation de cette reprise de contrôle manuel pourrait être plus ou moins difficile selon la situation et l’expérience d’interactions avec le système complètement automatisé. Nous avons examiné la manière dont cette manœuvre pourrait être apprise par des conducteurs, en testant l’effet de différentes formes d’entrainement sur la performance et la sécurité (temps de réponse et qualité de la reprise de contrôle). L’acceptabilité et la confiance, les attitudes des conducteurs, les intentions d’utilisation du système de conduite complètement automatisée et l’impact de ces variables sur les comportements dans le véhicule ont été mesurés. / Fully automated cars could possibly be on the road in the decades to come. They will allow drivers to be driven by an informatics system in their own vehicle. Such an innovation could lead to a revolution that would change the driver’s status and its activities during the trips, but also the infrastructure, freight, some professions, etc. Nowadays, these vehicles are not available for sale yet, and it is difficult to forecast accurately when they will be, and also what their features will be. Considering this, one of the aims of the present thesis is to examine to what extend fully automated driving will be accepted. Even though the driver is driven by its vehicle, he could have to resume manual control in different circumstances. Indeed, this maneuver could be performed in an emergency or in an anticipated situation while he could be engaged in a non driving-related activity. Performing a manual control recovery could be more or less difficult according to the situation and the experience with the fully automated system. The way this maneuver could be learned by drivers has been examined, testing the impact of different kinds of training on performance and safety (response time and control recovery quality). Acceptability, trust, drivers’ attitudes, intentions to use the fully automated driving system and the impact of these variables on behaviors inside the vehicle have been assessed.