Human-Machine Interface Development For Modifying Driver Lane Change Behavior In Manual, Automated, And Shared Control Automated Driving

Rear-end crashes are common on U.S. roads. Driver assistance and automated driving technologies can reduce rear-end crashes (among other crash types as well). Braking is assumed for forward collision warning (FCW) and automatic emergency braking (AEB) systems. Braking is also used for adaptive cruise control (ACC) and in automated driving systems more generally. However, steering may be advised in an emergency if the adjacent lane is clear and braking is unlikely to avoid a collision. Steering around an obstacle when feasible also eliminates the risk of becoming the new forward collision hazard. Driver assist technology like emergency steer assist (ESA) and Level 2 or Level 3 automated driving systems might facilitate manual emergency lane changes but may require the driver to manually initiate the maneuver, something which drivers are often reluctant to do. An Human-Machine Interface (HMI) might advise the driver of a steerable path when feasible in forward collision hazard situations. Such an HMI might also advise a driver of normal lane change opportunities that can reduce travel time, increase fuel efficiency, or simply enhance the driving experience by promoting `flow.\u27 This dissertation investigated the propensity of drivers to brake only versus steer in both manual and automated driving situations that end in a high-intensity forward collision hazard. A audio-visual Field of Safe Travel (FOST) cluster display and haptic steering wheel HMI were developed to advise drivers in both discretionary and emergency situations of a lane change opportunity. The HMI was tested in a moving base simulator in manual driving, in fully autonomous driving, and in shared-control autonomous driving during a simulated highway commute that ended in an high-intensity forward collision hazard situation. Results indicated that a) driver response was affected by the nature of the automated driving (faster response in hands-on shared control versus hands-off fully autonomous driving); b) exposure to the HMI in normal lane changes both familiarized the driver with the HMI and introduced a mental set that steering was also a possibility rather than braking only; c) but that drivers used their direct vision to determine their response in the emergency event. A methodological issue related to mental set was also uncovered and resolved through screening studies. The final study brought the dissertation full-circle, comparing hands-off fully automated driving to hands-on shared control automated driving in the context of either providing some or no exposure to the developed LCA system concept. Results of the final study indicated that shared control lies somewhere between that of manual driving and hands-off fully automate driving. Benefits were also shown to exist for the LCA system concept irrespective of whether the discrete haptic profiles are included or not. The discrete haptic profiles did not statistically reliably increase response times to the FC hazard event, although they do show a trend toward decreasing response variability. This finding solidified the fact that by implementing a system for benign driving that aids in establishing a mental set to steer around an obstacle may actually be beneficial for rear-end crash scenarios. This dissertation’s contributions include a) audio-visual FOST display concepts; b) discrete haptic steering display concepts; c) a paired-comparisons scaling for urgency for haptic displays applied while driving; d) a new ``mirage scenario\u27\u27 methodology for eliciting subjective assessments in the context of a forward collision hazard, briefly presented then removed, without risk of simulator sickness, and e) a methodological lesson for others who wish to investigate semi-automated and automated driving interventions and must manage driver mental set carefully

Improving Drivers’ Behaviour When Partial Driving Automation Fails

With the advent of automated vehicle systems, the role of drivers has changed to a more supervisory role. However, it is known that all vehicles with Level 2 (L2) systems have a very specific operational design domain (ODD) and can only function on limited conditions. Hence, it is important for drivers to perceive the situations properly and regain the control from the L2 system when needed. As suggested by past research, designing an informative interface could help drivers in their new supervision and intervention role while driving with L2 vehicles by providing feedback to drivers when hazards or event that may cause system failure are detected. On the other hand there are many situations where these vehicles cannot detect hazards and provide any feedback prior to the event. In these cases, training programs which provide drivers with an experience of these system limitations and allow them to practice dealing with such limitations can prove to be effective countermeasures. The objective of the current study is to employ different methods (designing HMI and training drivers) to increase drivers’ situational awareness regarding operational design domain (ODD) and improve drivers performance in transfer of control situations while driving with level 2 (L2) automation features. This study includes two experiments- in first experiment, an informative dashboard interface was designed and tested through three phases (observation, prototyping, testing). Results from the testing phase showed that drivers who received the newly designed dashboards took back control more effectively and had more situational awareness compared to the control group. In the second experiment, a PC-based training program was designed and tested to improve drivers takeover response and situational awareness when L2 systems reach their ODD limits. Results showed drivers in the PC-based training group took back control more effectively when L2 systems reached their ODD limits and had more situational awareness compared to the drivers who received user manual or placebo training

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Ambient hues and audible cues: An approach to automotive user interface design using multi-modal feedback

The use of touchscreen interfaces for in-vehicle information, entertainment, and for the control of comfort settings is proliferating. Moreover, using these interfaces requires the same visual and manual resources needed for safe driving. Guided by much of the prevalent research in the areas of the human visual system, attention, and multimodal redundancy the Hues and Cues design paradigm was developed to make touchscreen automotive user interfaces more suitable to use while driving. This paradigm was applied to a prototype of an automotive user interface and evaluated with respects to driver performance using the dual-task, Lane Change Test (LCT). Each level of the design paradigm was evaluated in light of possible gender differences. The results of the repeated measures experiment suggests that when compared to interfaces without both the Hues and the Cues paradigm applied, the Hues and Cues interface requires less mental effort to operate, is more usable, and is more preferred. However, the results differ in the degradation in driver performance with interfaces that only have visual feedback resulting in better task times and significant gender differences in the driving task with interfaces that only have auditory feedback. Overall, the results reported show that the presentation of multimodal feedback can be useful in design automotive interfaces, but must be flexible enough to account for individual differences

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Readiness Assessment of Engineering Practices for Designing Autonomous Industrial Mobile Machinery

Autonomous industrial mobile machines have emerged as proof-of-concept demonstrations in various industries in the past years and commercialized in some industries as well. Advances in automation engineering has developed to a stage where the automated machines are called as autonomous machines with varying degrees of autonomy from semi-automated functions to full autonomy. Advances in maturity of technologies providing autonomous functions have enabled new developments in designing autonomous mobile machines. In addition to the advances in technologies providing autonomous functions, advances in maturity of engineering practices has enabled development in the autonomous technologies. This thesis aims to assess readiness factors of engineering practices for designing autonomous industrial mobile machinery. The readiness factors consider elements that are required of engineering practices to design autonomous mobile machinery. Such assessment includes presenting focal challenges in autonomous systems design, solutions and methods for over-coming those challenges and important considerations for design. The research objectives are based on a hypothesis that the new technologies which provide autonomous functions result in new design requirements for engineering practices. The research objective is to discover what does the increasing autonomy produce for the design of the machine. The methods include a literature research, which presents design considerations found from varying fields of autonomous systems development (aerial, maritime, road vehicle and mobile machines), and an inter-view research, which presents an anonymized interpretation of eight thematic interviews find-ings. Interviews were conducted with mobile machine original equipment manufacturers and system integrator companies interested in the development of autonomous mobile machines. Based on the literature and interview researches, a set of readiness elements and factors are assessed as a summary. It can be concluded, that the engineering practices should consider a new operational principle of an autonomous system that must include clear benefits compared to the operation principle of a regular machine. The benefits can be gained from varying levels of autonomy. Readiness factors include considerations for a new set of competences, managing an interdisciplinary design process, importance of partnerships and ecosystems with longevity, and advanced use of digital design practices and physical test environments that complement each other and the design process of autonomous functions

Enhancing user experience and safety in the context of automated driving through uncertainty communication

Operators of highly automated driving systems may exhibit behaviour characteristic of overtrust issues due to an insufficient awareness of automation fallibility. Consequently, situation awareness in critical situations is reduced and safe driving performance following emergency takeovers is impeded. Previous research has indicated that conveying system uncertainties may alleviate these issues. However, existing approaches require drivers to attend the uncertainty information with focal attention, likely resulting in missed changes when engaged in non-driving-related tasks. This research project expands on existing work regarding uncertainty communication in the context of automated driving. Specifically, it aims to investigate the implications of conveying uncertainties under consideration of non-driving-related tasks and, based on the outcomes, develop and evaluate an uncertainty display that enhances both user experience and driving safety. In a first step, the impact of visually conveying uncertainties was investigated under consideration of workload, trust, monitoring behaviour, non-driving-related tasks, takeover performance, and situation awareness. For this, an anthropomorphic visual uncertainty display located in the instrument cluster was developed. While the hypothesised benefits for trust calibration and situation awareness were confirmed, the results indicate that visually conveying uncertainties leads to an increased perceived effort due to a higher frequency of monitoring glances. Building on these findings, peripheral awareness displays were explored as a means for conveying uncertainties without the need for focused attention to reduce monitoring glances. As a prerequisite for developing such a display, a systematic literature review was conducted to identify evaluation methods and criteria, which were then coerced into a comprehensive framework. Grounded in this framework, a peripheral awareness display for uncertainty communication was developed and subsequently compared with the initially proposed visual anthropomorphic uncertainty display in a driving simulator study. Eye tracking and subjective workload data indicate that the peripheral awareness display reduces the monitoring effort relative to the visual display, while driving performance and trust data highlight that the benefits of uncertainty communication are maintained. Further, this research project addresses the implications of increasing the functional detail of uncertainty information. Results of a driving simulator study indicate that particularly workload should be considered when increasing the functional detail of uncertainty information. Expanding upon this approach, an augmented reality display concept was developed and a set of visual variables was explored in a forced choice sorting task to assess their ordinal characteristics. Particularly changes in colour hue and animation-based variables received high preference ratings and were ordered consistently from low to high uncertainty. This research project has contributed a series of novel insights and ideas to the field of human factors in automated driving. It confirmed that conveying uncertainties improves trust calibration and situation awareness, but highlighted that using a visual display lessens the positive effects. Addressing this shortcoming, a peripheral awareness display was designed applying a dedicated evaluation framework. Compared with the previously employed visual display, it decreased monitoring glances and, consequentially, perceived effort. Further, an augmented reality-based uncertainty display concept was developed to minimise the workload increments associated with increases in the functional detail of uncertainty information.</div

Conceptualising automated driving shared control hazard causes

The motivation for this research was the realisation that the introduction of greater vehicle automation would not only change the task of driving but would also potentially change how vehicles are developed and safety is assured. Undertaking a practice-based workshop identified many Automated Driving (AD) safety assurance challenges having different levels of human-machine control. These challenges include an increase in the size and complexity of AD safety analyses, a need to re-examine the notion of controllability in the context of shared control, and the need to conceptualise the vehicle as a system of systems. To begin addressing these challenges and to answer the research question “how can the safety of AD be assured under different levels of shared control?” this research has created three products: a vehicle model and behavioural competency taxonomy that allows AD shared control to be conceptualised, a concrete hazard analysis method for analysing AD shared control hazard causes, and a safety case argument pattern for that. A series of case studies evaluate the research products described above. These cases have used contemporary AD vehicle features, having varying levels of automation. The evaluation of driver assistance, partial and conditional automation cases have been completed by the author. Complementing these is the analysis of a highly automated vehicle system, which has been undertaken with the engineering team from Oxbotica. Considered together these case studies establish the research products as a proof-of-concept hazard analysis method for AD shared control. Further evaluation work is needed to test the viability of the method as an engineering tool for use by automotive practitioners working in a product development environment