In most levels of vehicle automation, drivers will not be merely occupants or passengers of automated vehicles. Especially in lower levels of automation, where the driver is still required to serve as a fallback level (SAE L3) or even as a supervisor (SAE L2), there is a need to communicate relevant system states (e.g., that the automated driving system works reliably or that there is a need for manual intervention) via the Human-Machine Interface (HMI). However, there are currently no agreed-upon guidelines that apply specifically to HMIs for automated driving. In this paper, we summarize design recommendations for visual-auditory and visual-vibrotactile HMIs derived from empirical research, applicable standards and design guidelines pertaining to in-vehicle interfaces. On this basis, we derive an initial set of principles and criteria for guiding the development and design of automated vehicle HMIs. A heuristic evaluation methodology consisting of an itemized checklist evaluation that can be used to verify that basic HMI requirements formulated in the guidelines are met is also presented. The heuristic evaluation involves an inspection of the HMI during typical use cases, judging their compliance with the proposed guidelines and documentation of identified instances of non-compliance. Taken together, the combination of the proposed guidelines and the heuristic evaluation methodology form the basis for both design and validation recommendations of automated vehicle HMIs, that can serve the industry in the important evolution of automation within vehicles.

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this. Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA’s automated vehicle policy are met. Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics. Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.

Class 8 heavy-duty truck platooning has demonstrated significant fuel economy benefits on routes with road grade less than±2% in literature, but there is little to no platooning research on routes with road grade greater than±2% - which make up a significant portion of U.S. highways. Therefore, the effort described in this thesis is aimed at assessing currently available two-truck platoon control strategies as well as developing new strategies to improve platoon performance on hilly terrain. Specifically, the strategies tested in this work include four types of lead truck speed control strategies and two types of platoon transmission shifting strategies. These strategies are tested using two experimentally validated heavy-duty, two-truck platoon simulation approaches where each approach has its own advantages and disadvantages. The trends observed from these two simulation approaches indicate that the lead truck speed control and transmission shifting strategies have a significant effect on the platoon fuel economy and gap control performance when the platoon operates on a hilly terrain route.

Scenario-based testing emerges as the main approach to validate automated driving systems (ADS) and thus ensure safe road traffic. Thereby, the test scenarios used should represent the traffic event of the corresponding operational design domain (ODD) and should cover the traffic situation from normal driving to an accident. For this, the fusion of police accident data and video-based traffic observation data into one database for subsequent scenario generation is advisable. Therefore, this paper presents the Fuse4Representativity (Fuse4Rep) process model as part of the Dresden Method, which helps to fuse heterogeneous data sets into one ODD-representative database for lean, fast and comprehensive scenario generation. Hereby, statistical matching is used as the fusion approach building on probable matching variables, such as the 3-digit accident type, the collision type and the misconduct of participants. Moreover, the paper shows how the scenarios generated in this way can be hypothetically used to validate ADS, e.g. in a stochastic traffic simulation incorporating human driver behaviour models. Future studies should apply the Fuse4Rep model in practice and test its validity.

Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs.

Scenario-based testing emerges as the main approach to validate automated driving systems (ADS) and thus ensure safe road traffic. Thereby, the test scenarios used should represent the traffic event of the corresponding operational design domain (ODD) and should cover the traffic situation from normal driving to an accident. For this, the fusion of police accident data and video-based traffic observation data into one database for subsequent scenario generation is advisable. Therefore, this paper presents the Fuse4Representativity (Fuse4Rep) process model as part of the Dresden Method, which helps to fuse heterogeneous data sets into one ODD-representative database for lean, fast and comprehensive scenario generation. Hereby, statistical matching is used as the fusion approach building on probable matching variables, such as the 3-digit accident type, the collision type and the misconduct of participants. Moreover, the paper shows how the scenarios generated in this way can be hypothetically used to validate ADS, e.g. in a stochastic traffic simulation incorporating human driver behaviour models. Future studies should apply the Fuse4Rep model in practice and test its validity.

Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems. / M.S. / Increasingly, many of today’s vehicles offer automated driving technology (i.e., Assisted Driving) that support the driver through steering, braking, and accelerating the vehicle. However, during this level of automation the driver must be prepared to take control of the vehicle, requiring them to monitor the environment and the automated driving system. In addition, a limited number of vehicles offer automated driving technology (i.e., Automated Driving) that controls the vehicle and does not require the driver to monitor the environment or system, however, the driver must take control when prompted by the system. Vehicles capable of Automated Driving can also operate in Assisted and manual driving modes. Given the ability of Automated Driving vehicles to operate in Assisted and manual driving, it is important to consider driver’s ability to follow and predict the behavior of the automated system. In addition, since Automated Driving does not require drivers to keep their eyes or mind on driving or monitoring the road, drivers are allowed to perform secondary tasks. Since drivers are free to choose what types of tasks they do during Automated Driving, it is important to understand whether drivers will choose to engage in Secondary tasks, and their ability to perform these tasks during Automated Driving. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in tasks, and their ability to perform scheduled smartphone tasks, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method. The WoZ method uses a concealed human to simulate an automated computer system, in this case an automated driving system. A total of 36 drivers completed the on-road experiment. The participants experienced periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ knowledge of who/what was controlling the vehicle and the driver’s role in the current automated mode was measured. Drivers’ performance of tasks during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in tasks during automated driving, participants were allowed to freely choose to engage in smartphone tasks between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results showed that participants’ ability to identify their role in Automated Driving increased from the first time they experienced the system to the subsequent times. There was no change in drivers’ willingness to engage in tasks or drivers’ ability to perform tasks as they gained more experience with the Automated Driving system. However, the level of task engagement indicated that drivers were immediately willing to engage in tasks during Automated Driving. Drivers also showed a high-level of task performance. Taken together, these findings indicate that drivers are willing to engage in and perform non-driving related tasks during Automated Driving. These findings can help guide future research focused on automated systems and the design of automated driving systems.

Objectives: Research suggests the general public has a lack of faith in highly automated vehicles (HAV) stems from a lack of system transparency while in motion (e.g., the user not being informed on roadway perception or anticipated responses of the car in certain situations). This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to debut, and when the user has minimal training on, and control over, the vehicle. To improve user trust and their perception of comfort and safety, this study aimed to develop more detailed and tailored human-machine interfaces (HMI) aimed at relying automated vehicle intended actions (i.e., "intentions") and perceptions of the driving environment to the user. Methods: This project developed HMI systems, with a focus on visual and auditory displays, and implemented them into a HAV developed at the Virginia Tech Transportation Institute (VTTI). Volunteer participants were invited to the Smart Roads at VTTI to experience these systems in real-world driving scenarios, especially ones typically found in rideshare or public transit operations. Participant responses and opinions about the HMIs and their perceived levels of comfort, safety, trust, and situational awareness were captured via paper-based surveys administered during experimentation. Results: There was a considerable link found between HMI modality and users' reported levels of comfort, safety, trust, and situational awareness during experimentation. In addition, there were several key behavioral factors that made users more or less likely to feel comfortable in the HAV. Conclusions: Moving forward, it will be necessary for HAVs to provide ample feedback to users in an effort to increase system transparency and understanding. Feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle states to users. / Master of Science / One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle "intentions" and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads.

Systeme zum automatisierten Fahren erlauben es, die Fahrzeugführung in einem gewissen Maß vom Fahrer an das Fahrzeug zu übertragen. Da der Fahrer auf diese Weise unterstützt, entlastet oder sogar ersetzt werden kann, werden Systeme zum automatisierten Fahren mit einem großen Potential für Verbesserungen hinsichtlich Straßenverkehrssicherheit, Fahrkomfort und Effizienz verbunden - vorausgesetzt, dass diese Systeme angemessen benutzt werden. Systeme zum hochautomatisierten Fahren stellen in diesem Zusammenhang eine besondere Herausforderung für die Mensch-Maschine-Interaktion dar: So wird es dem Fahrer bei diesem Automatisierungsgrad zwar zum ersten mal ermöglicht, das System nicht mehr permanent überwachen zu müssen und somit die Fahrtzeit potentiell für fahrfremde Tätigkeiten zu nutzen. Es wird jedoch immer noch erwartet, dass der Fahrer nach einer vorherigen angemessenen Übernahmeaufforderung die Fahrzeugführung im Bedarfsfall gewährleisten kann. Angemessenes Automatisierungsvertrauen stellt daher eine zentrale Komponente für die erfolgreiche Kooperation zwischen Fahrern und Systemen zum hochautomatisierten Fahren dar und sollte bei der Gestaltung derartiger Systeme berücksichtigt werden. Frühere Befunde weisen beispielsweise bereits darauf hin, dass unterschiedliche Informationen über automatisierte Systeme ein möglicher Ansatz sein könnten um das Automatisierungsvertrauen des Fahrers aktiv zu gestalten. Automatisierungsvertrauen als Variable in der Gestaltung von Fahrzeugtechnologie zu berücksichtigen erfordert jedoch zunächst auch in der Lage zu sein, Automatisierungsvertrauen adäquat messen zu können. In diesem Sinne war die Zielsetzung dieser Arbeit einerseits die Untersuchung verschiedener Methoden zur Messung des Automatisierungsvertrauens des Fahrers sowie andererseits die Identifikation, prototypische Umsetzung und Bewertung potentieller Ansätze zur Gestaltung von Automatisierungsvertrauen im Kontext von Systemen zum hochautomatisierten Fahren. Zu diesem Zweck wurden drei Fahrsimulatorstudien mit insgesamt N = 280 Probanden durchgeführt. Die vorliegenden Ergebnisse weisen darauf hin, dass (i) sowohl Selbstberichtsverfahren als auch Verhaltensmaße prinzipiell dazu verwendet werden können um das Automatisierungsvertrauen des Fahrers in Systeme zum hochautomatisierten Fahren zu operationalisieren, (ii) eine vorherige Auseinandersetzung mit funktionalen Grenzen von Systemen zum hochautomatisierten Fahren einen nachhaltigen Effekt auf das Automatisierungsvertrauen des Fahrers in das System haben kann und (iii) insbesondere Informationen über die Funktionsweise von Systemen zum hochautomatisierten Fahren das Automatisierungsvertrauen des Fahrers in derartige Systeme verbessern können. Damit liefert die vorliegende Arbeit sowohl wertvolle Ansatze zur Messbarmachung als auch Hinweise für die Gestaltung von Automatisierungsvertrauen im Kontext des hochautomatisierten Fahrens. Darüber hinaus können die Befunde dieser Arbeit in gewissem Maße auch auf andere Arten von Fahrzeugautomatisierung sowie unterschiedliche Domänen und Anwendungen von Automatisierung übertragen werden. / Automated driving systems allow to transfer a certain degree of vehicle control from the driver to a vehicle. By assisting, augmenting or even supplementing the driver, automated driving systems have been associated with enormous potential for improving driving safety, comfort, and efficiency - provided that they are used appropriately. Among those systems, conditional automated driving systems are particularly challenging for human-automation interaction: While the driver is no longer required to permanently monitor conditional automated driving systems, he / she is still expected to provide fallback performance of the dynamic driving task after adequate prior notification. Therefore, facilitating appropriate automation trust is a key component for enabling successful cooperation between drivers and conditional automated driving systems. Earlier work indicates that providing drivers with proper information about conditional automated driving systems might be one promising approach to do this. Considering the role of automation trust as a variable in the design of vehicle technology, however, also requires that drivers` automation trust can be viably measured in the first place. Accordingly, the objectives of this thesis were to explore difffferent methods for measuring drivers` automation trust in the context of conditional automated driving as well as the identification, implementation and evaluation of possible approaches for designing drivers` automation trust in conditional automated driving systems. For these purposes, three driving simulator studies with N = 280 participants were conducted. The results indicate that (i) both self-report measures and behavioral measures can be used to assess drivers` automation trust in conditional automated driving systems, (ii) prior familiarization with system limitations can have a lasting effffect on drivers` automation trust in conditional automated driving systems and (iii) particularly information about the processes of conditional automated driving systems might promote drivers` automation trust in these systems. Thus, the present research contributes much needed approaches to both measuring and designing automation trust in the context of conditional automated driving. In addition, the current findings might also be transferred to higher levels of driving automation as well as other domains and applications of automation.

Systeme zum automatisierten Fahren erlauben es, die Fahrzeugführung in einem gewissen Maß vom Fahrer an das Fahrzeug zu übertragen. Da der Fahrer auf diese Weise unterstützt, entlastet oder sogar ersetzt werden kann, werden Systeme zum automatisierten Fahren mit einem großen Potential für Verbesserungen hinsichtlich Straßenverkehrssicherheit, Fahrkomfort und Effizienz verbunden - vorausgesetzt, dass diese Systeme angemessen benutzt werden. Systeme zum hochautomatisierten Fahren stellen in diesem Zusammenhang eine besondere Herausforderung für die Mensch-Maschine-Interaktion dar: So wird es dem Fahrer bei diesem Automatisierungsgrad zwar zum ersten mal ermöglicht, das System nicht mehr permanent überwachen zu müssen und somit die Fahrtzeit potentiell für fahrfremde Tätigkeiten zu nutzen. Es wird jedoch immer noch erwartet, dass der Fahrer nach einer vorherigen angemessenen Übernahmeaufforderung die Fahrzeugführung im Bedarfsfall gewährleisten kann. Angemessenes Automatisierungsvertrauen stellt daher eine zentrale Komponente für die erfolgreiche Kooperation zwischen Fahrern und Systemen zum hochautomatisierten Fahren dar und sollte bei der Gestaltung derartiger Systeme berücksichtigt werden. Frühere Befunde weisen beispielsweise bereits darauf hin, dass unterschiedliche Informationen über automatisierte Systeme ein möglicher Ansatz sein könnten um das Automatisierungsvertrauen des Fahrers aktiv zu gestalten. Automatisierungsvertrauen als Variable in der Gestaltung von Fahrzeugtechnologie zu berücksichtigen erfordert jedoch zunächst auch in der Lage zu sein, Automatisierungsvertrauen adäquat messen zu können. In diesem Sinne war die Zielsetzung dieser Arbeit einerseits die Untersuchung verschiedener Methoden zur Messung des Automatisierungsvertrauens des Fahrers sowie andererseits die Identifikation, prototypische Umsetzung und Bewertung potentieller Ansätze zur Gestaltung von Automatisierungsvertrauen im Kontext von Systemen zum hochautomatisierten Fahren. Zu diesem Zweck wurden drei Fahrsimulatorstudien mit insgesamt N = 280 Probanden durchgeführt. Die vorliegenden Ergebnisse weisen darauf hin, dass (i) sowohl Selbstberichtsverfahren als auch Verhaltensmaße prinzipiell dazu verwendet werden können um das Automatisierungsvertrauen des Fahrers in Systeme zum hochautomatisierten Fahren zu operationalisieren, (ii) eine vorherige Auseinandersetzung mit funktionalen Grenzen von Systemen zum hochautomatisierten Fahren einen nachhaltigen Effekt auf das Automatisierungsvertrauen des Fahrers in das System haben kann und (iii) insbesondere Informationen über die Funktionsweise von Systemen zum hochautomatisierten Fahren das Automatisierungsvertrauen des Fahrers in derartige Systeme verbessern können. Damit liefert die vorliegende Arbeit sowohl wertvolle Ansatze zur Messbarmachung als auch Hinweise für die Gestaltung von Automatisierungsvertrauen im Kontext des hochautomatisierten Fahrens. Darüber hinaus können die Befunde dieser Arbeit in gewissem Maße auch auf andere Arten von Fahrzeugautomatisierung sowie unterschiedliche Domänen und Anwendungen von Automatisierung übertragen werden. / Automated driving systems allow to transfer a certain degree of vehicle control from the driver to a vehicle. By assisting, augmenting or even supplementing the driver, automated driving systems have been associated with enormous potential for improving driving safety, comfort, and efficiency - provided that they are used appropriately. Among those systems, conditional automated driving systems are particularly challenging for human-automation interaction: While the driver is no longer required to permanently monitor conditional automated driving systems, he / she is still expected to provide fallback performance of the dynamic driving task after adequate prior notification. Therefore, facilitating appropriate automation trust is a key component for enabling successful cooperation between drivers and conditional automated driving systems. Earlier work indicates that providing drivers with proper information about conditional automated driving systems might be one promising approach to do this. Considering the role of automation trust as a variable in the design of vehicle technology, however, also requires that drivers` automation trust can be viably measured in the first place. Accordingly, the objectives of this thesis were to explore difffferent methods for measuring drivers` automation trust in the context of conditional automated driving as well as the identification, implementation and evaluation of possible approaches for designing drivers` automation trust in conditional automated driving systems. For these purposes, three driving simulator studies with N = 280 participants were conducted. The results indicate that (i) both self-report measures and behavioral measures can be used to assess drivers` automation trust in conditional automated driving systems, (ii) prior familiarization with system limitations can have a lasting effffect on drivers` automation trust in conditional automated driving systems and (iii) particularly information about the processes of conditional automated driving systems might promote drivers` automation trust in these systems. Thus, the present research contributes much needed approaches to both measuring and designing automation trust in the context of conditional automated driving. In addition, the current findings might also be transferred to higher levels of driving automation as well as other domains and applications of automation.