HMI Design Research Articles

Fostering User Acceptance in Shared Autonomous Vehicles: A Framework for HMI Design

Fostering User Acceptance in Shared Autonomous Vehicles: A Framework for HMI Design

The Anxiety Consumers Feel About Using Robotaxis: HMI Design for Anxiety Factor Analysis and Anxiety Relief Based on Field Tests

The Anxiety Consumers Feel About Using Robotaxis: HMI Design for Anxiety Factor Analysis and Anxiety Relief Based on Field Tests

Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust

Advances in autonomous driving provide an opportunity for AI-assisted driving instruction that directly addresses the critical need for human driving improvement. How should an AI instructor convey information to promote learning? In a pre-post experiment (n = 41), we tested the impact of an AI Coach’s explanatory communications modeled after performance driving expert instructions. Participants were divided into four (4) groups to assess two (2) dimensions of the AI coach’s explanations: information type (‘what’ and ‘why’-type explanations) and presentation modality (auditory and visual). We compare how different explanatory techniques impact driving performance, cognitive load, confidence, expertise, and trust via observational learning. Through interview, we delineate participant learning processes. Results show AI coaching can effectively teach performance driving skills to novices. We find the type and modality of information influences performance outcomes. Differences in how successfully participants learned are attributed to how information directs attention, mitigates uncertainty, and influences overload experienced by participants. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, results support the need to align communications with human learning and cognitive processes. We provide eight design implications for future autonomous vehicle HMI and AI coach design.

Exploring remote operation of heavy vehicles—findings from a simulator study

AbstractFully automated driving has posed more challenges than expected, and remote operation of heavy vehicles is increasingly getting attention. Therefore, human remote operators may have an essential role in compensating for the technological shortcomings in vehicle automation. This poses challenges in designing the work of human remote operators of automated heavy vehicles. This paper present findings from a research project performed in collaboration between the RISE Research Institutes of Sweden and Scania. In the project, human-automation interaction requirements and challenges for remote operator work were explored through a simulator study. Before the study, three main operator tasks were defined: assessment, assistance, and remote driving. The simulation occurred in a transportation scenario where operators handled ten trucks driving on a public road and in confined areas (transportation hub). Fifteen participants completed the study. The results provide examples and insights into classical automation-related challenges in a new context—the remote operation of heavy vehicles. Instances of challenges with situational awareness, out-of-the-loop, trust, and attention management were found and are discussed in relation to HMI design and requirements. In addition, it was found that transitions between relatively passive monitoring and more active assistance and driving were performed more fluently than expected. In general, supervisory control of ten vehicles in parallel was seen as a feasible task given the conditions in the simulated environment.

An Elderly-Oriented Design of HMI in Autonomous Driving Cars Based on Rough Set Theory and Backpropagation Neural Network

An Elderly-Oriented Design of HMI in Autonomous Driving Cars Based on Rough Set Theory and Backpropagation Neural Network

DESIGN AND DEVELOPMENT OF HMI IN SIMULATOR FOR FRESH FUEL HANDLING SYSTEM

KALBRSIM operator training simulator is used for training Prototype Fast Breeder Reactor (PFBR) nuclear power plant operators. In simulator, using virtual panel HMI which is an exact replica of actual hardware panels, all the essential fresh fuel operations can be done. Visualization power is also provided by 3D models which aid in quicker understanding of the complex process involved in Fresh Fuel Handling System (FFHS). Operation using Virtual Panel is an excellent tool for system developers to test the logic and process models of FFHS in KALBRSIM before integration with hardware panels. Also, operator training through virtual panel HMI can enable operator to monitor and control FFHS by running multiple screens at once at a central location. Teaching operators their effect of actions using 3D models is an add on with virtual panels which help the operator visualize the system accurately. This paper discuss the design and development of virtual panel HMI and 3D models of fresh fuel handling system used in full scope replica PFBR operator training simulator. Advantage of using HMI in simulator is also covered.

Research on the Optimization and Evaluation of Excavator Human-Machine Interface Layout Based on Situation Awareness

In order to solve the problem of a large number of types and complex layouts of control devices on the operator interface of mining excavators, and a large variety and number of control elements on the control panel, the layout optimization design is carried out by applying the theoretical model of situational awareness and the influencing factors to the layout design of the human-machine interface. The modularity of the interface and the hierarchy of the original components are divided, and the importance evaluation indicators established according to situational awareness are weighted according to the entropy weighting method, and then the results are combined with the VIKOR method to analyse the importance of the interface components and the control panel module respectively before the interface is optimised and designed. The results show that the application of situational awareness theory to HMI design can play an important role in enhancing the level of situational awareness of operators and meeting their needs.

KUKA KR3 Industrial Robot Unit HMI Design and Control with Universal Industrial PC in Sipy Development Environment

The challenges presented by Industry 4.0 require an innovative, new type of thinking from the engineers. Connecting old machines into a network presents significant problems, as the switch cabinets for these are obsolete and non-upgradeable. This has made developing a solution necessary, that is later universally applicable for scrapped, but mechanically intact robot arms. During the realization of the project such an IoT compatible graphical interface was developed, which can control a KUKA KR3 industrial robot unit and can also Real-Time simulate it in virtual reality. The security functions of the robot are provided by a Pycom SiPy developer panel.

파이썬을 이용한 변전소 고장복구 지원시스템의 HMI 설계

Since the propagation of faults in substations entails social losses due to power outages, in case a fault event occurs, the fault recovery time should be shortened by quickly and accurately identifying the faults. Although breakdowns do not occur frequently due to the modernization of substation facilities, it is necessary to prepare for faults recovery through operation of the standard recovery procedure and education and training programs. In the future substation fault recovery, various solutions should be developed to detect various fault causes and new fault types considering the complexity of the system. Recently, fault recovery using AI techniques is being attempted. In this paper, as a part of the basic research results of fault recovery support system using intelligent technique for digital substation, we propose the HMI design of a 154kV substation fault recovery support system using Python language. After training 15 types of fault types in DNN, the proposed fault recovery support system identifies fault types with new test patterns. Next, the HMI of the fault recovery support system is designed to output the fault recovery procedure through the Rule-based Expert System. Finally, the verification of the designed HMI through simulation is described.

Human Factors in Automotive HMI Design

Driving is an information processing activity. In the process of driving, the driver constantly obtains and processes information from various sensory channels to make decisions and take appropriate actions to control vehicle movement. This paper will discuss four parts: sensory, cognition, decision making and automation.

Icon format on the in-vehicle HMI affects drivers’ cognitive performance

ABSTRACT Although there have been many studies on icon preferences and search performances in the past, only a few principles, criteria, and guidelines may be applicable to the design of icons in a time- and safety-critical interaction (driving) context. This study combines a familiarity training task with an icon-semantic meaning recall task to explore the effects of icon format, concreteness and familiarity on drivers’ icon cognitive performance. Icons in 3 formats were created as experimental stimuli. Subsequently, 39 participants were divided into three groups, and each group was required to complete a familiarity training task with a specific icon format. After the participants were familiarised with the icons over two training sessions, their abilities to recall relevant semantic meanings when seeing icons were tested. The results indicated that participants responded more accurately and quickly to the icons in the graphic & keyword format than to the icons in the other two formats. However, the differences between different formats greatly decreased with an increase in familiarity. In addition, compared with concrete icons, abstract icons had an advantage in recognition speed, especially when the icons were unfamiliar. The findings in this study provide practical guidelines for the icon design of in-vehicle HMI.

Adaptive User Experience in the Car—Levels of Adaptivity and Adaptive HMI Design

Advancements in driver state detection and artificial intelligence allow for more and more user-centred and individual experiences. Intelligence and adaptivity in the vehicle context address the three main goals: Increasing safety, usability and empathy in vehicle systems. Adaptivity of systems can be evaluated by considering the technical system features, user-interface-related features and the actual user experience of the adaptive system. We provide an overview of classifications for adaptive systems including the Levels of Adaptive Sensitive Responses (LASR). The levels differentiate the input that a system considers in its operations to adapt to user groups or to the individual user. Along with that, we propose User Experience (UX) design guidelines applicable to the different levels. In an online survey, we varied LASR and one of the UX design guidelines, namely transparency. The within-subjects study showed that both, the levels and the variation of transparency, influenced the perception of intelligence, transparency and intuitive design. However, a significant proportion of users did not understand the difference between the two LASR versions, indicating that users build mental models of systems that imply more personal data usage than the system actually employs. The LASR framework allowed this differentiation to be revealed in system performance and user perception. More research is necessary to elaborate the correlation between levels of adaptivity, UX design, specific UX design guidelines and user experience measures.

Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment

Mental, physical, and emotional workload is the main source for the driver’s stress, fatigue, and distraction, which affects the driving tasks performed by the driver in terms of responding to the surroundings and the delay in reacting to events. Driver behaviour is an uncertain element within Intelligent Transportation Systems (ITS). Thus, this work presents a mathematical model that accounts for three main elements in ADAS-assisted vehicles, autonomous vehicles, connected vehicles, and autonomous and connected vehicles. The model accounts for processing times by the On-Board Unit (OBU), driver time, which covers both perception and response times, and communication time, which covers exchanged messages between vehicles and/ or infrastructure. The model shows two distinct behaviours, power law trend in the case of ADAS and exponential trend in the case of autonomous and connected vehicles, with both power and exponential interacting in the case of connected vehicles. In all considered scenarios, the mathematical model presents a dynamically interacting usability function that contains probability, Gaussian interpolation, and value limiting functions. The work also shows the effect of each time component on the usability, and hence the effectiveness of avoiding incidents. The model helps in optimizing both HMI, electronics, and communication designs. Driver time, in the case of ADAS, connected vehicles, and under rare conditions in autonomous and connected vehicles is critical, hence HMI optimization is important even though in autonomous and connected vehicles is not always needed. The work shows that the best driving usability is in the case of autonomous and connected vehicles.

Trust in automated vehicles

<p id="p00005">Automated driving (AD) is one of the key directions in the intelligent vehicles field. Before full automated driving, we are at the stage of human-machine cooperative driving: Drivers share the driving control with the automated vehicles. Trust in automated vehicles plays a pivotal role in traffic safety and the efficiency of human-machine collaboration. It is vital for drivers to keep an appropriate trust level to avoid accidents. We proposed a dynamic trust framework to elaborate the development of trust and the underlying factors affecting trust. The dynamic trust framework divides the development of trust into four stages: dispositional, initial, ongoing, and post-task trust. Based on the operator characteristics (human), system characteristics (automated driving system), and situation characteristics (environment), the framework identifies potential key factors at each stage and the relation between them. According to the framework, trust calibration can be improved from three approaches: trust monitoring, driver training, and optimizing HMI design. Future research should pay attention to the following four perspectives: the influence of driver and HMI characteristics on trust, the real-time measurement and functional specificity of trust, the mutual trust mechanism between drivers and AD systems, and ways in improving the external validity of trust studies.

Design of HMI Based Digital Electric Bike Using DC/AC Power Converter with Regenerative Feature

The research project introduces an efficient HMI-based digitally controlled electric bike hardware model. Thesystem is designed using power electronics and LCD touch control. A low-cost model with regenerative features increases the acceptance of electric vehicles in Pakistan. The country is facing grave environmental issues and the transport sector has a major role in polluting the environment. Technology also promotes the environment-friendly electric vehicles Industry and reduces the growing pollution problem of the country. Economical transport with feasible milage is an essential need of the current time. Research provides the effective Electric-bike with digital security and improved mileage. A Six-Step DC/AC power converter drives the low consumption BLDC motor. The project analyzed the real-time performance of the electric vehicle on HMI. A regenerative braking system generates power while reducing the speed of a moving electric bike. The modeling andsimulation are performed using Proteus/Multisim simulating software. Results are verified using a hardware test model. The results will be helpful for design commercial advance HMI-based electric bikes in terms of motor requirement, machine behaviour, charging/discharging analysis at a different speed and torque, charging the battery while driving, and their limitations.

Design Guidelines for the Size and Length of Chinese Characters Displayed in the Intelligent Vehicle’s Central Console Interface

In order to ensure the driver’s safe driving, the human–computer interaction interface of an intelligent vehicle needs to convey important information. The text is an important carrier of this kind of information. The design criteria of English characters have been widely discussed, including the color, meaning, size and length. However, design guidelines for Chinese characters in central consoles of vehicles have rarely been discussed from a human–computer interaction perspective. In this paper, we investigated the size and the length of Chinese characters in the intelligent vehicle’s central control screen, based on international design guidelines and standards. The experiment involved 30 participants performing simulated in-vehicle secondary tasks. The result from the experiments shows that the usability of characters increases and the driver’s workload decreases as the characters get larger and shorter. We also propose a set of recommended values for the size and length of Chinese characters in this context. Future work will focus on providing design guidelines for other aspects of HMI design in intelligent vehicles.

Research on consumer demand and trend of automobile HMI function

Consumers pay more and more attention to automobile intelligence. Under the trend of “new four modernizations”, it is inevitable for automobiles to move towards intelligent terminals, and automobile HMI is an important carrier of automobile intelligence. At present, the design of automobile HMI is still not accurate enough to grasp the needs of consumers, which leads to poor consumer experience. In this paper, from the perspective of consumer demand, consumers are classified according to the difference of automobile HMI demand, and the experience evaluation and demand difference of different types of consumers on automobile HMI are analyzed to explore the demand trend of consumers on automobile HMI function.

A Virtual Reality-based Platform to Validate HMI Design for Increasing User’s Trust in Autonomous Vehicle

A Virtual Reality-based Platform to Validate HMI Design for Increasing User’s Trust in Autonomous Vehicle

HMI Design when Using Level 2 Automated Driving Function - Effects of System Status Presentation Considering the Risk of Malfunction on Driver Behavior –

This study examined the effect of system status presentation on driver behavior when driving with ACC and LKA, which are classified as level 2 automated driving. First, we analyzed the driving behavior of 40 test participants in a driving simulator study under three HMI conditions: without safety level, correct safety level, and incorrect safety level which does not work properly and becomes inactive. The driver behavior database constructed in this experiment, was used to quantify the accident avoidance probability under each HMI condition using the state transition probabilistic model proposed by the author in a previous study. Finally, we quantified the degree of reduction in the probability of accident occurrence when using this HMI device in consideration of the risk of malfunction based on the integrated error model proposed by the author. Based on these results, it was shown that the HMI device that acts as a real-time interface at the system safety level between the driver and the automated driving using ACC and LKA is effective in reducing traffic accidents regardless of the increased probability of traffic accidents due to malfunctions of HMI device.

Considerations for the development of a driver distraction safety rating system for new vehicles

Drivers engage in a wide range of non-driving related tasks while driving that have potential to distract to them and compromise their safety. These include interactions with infotainment systems built into the vehicle by vehicle manufacturers. These systems enable the performance of communication, entertainment, navigation and internet browsing tasks. Performing these tasks can degrade driving performance and increase crash risk. Not all infotainment technologies in new vehicles are equal in terms of their potential to distract. This paper documents the findings of a study commissioned by the Victorian Department of Transport to determine the feasibility of developing a test protocol for rating the distraction potential of new vehicles entering the Australian market. A literature review, consultation with expert international researchers and industry representatives, and workshops, were conducted in order to determine those elements of the HMI design of infotainment systems that should be assessed, identify suitable candidate test methods for assessing the visual and cognitive load imposed on drivers when performing infotainment tasks, and derive options for a distraction rating system. In addition, safety/rating assessment program reviews and a cost-benefit analysis of introducing a distraction rating system were undertaken. Eight potential distraction test methods were discerned from the literature and consultation. It was concluded that the most suitable test protocol for a distraction rating system involves the use of an HMI design checklist in combination with measurement of the visual and cognitive load imposed on drivers when performing specific infotainment tasks, using the VOT and DRT, respectively. Eight options for introducing a distraction safety rating as a consumer or NCAP distraction rating are presented. Each option builds upon the previous, with the first option being the development of voluntary guidelines (where vehicle manufacturers work to these guidelines on a voluntary basis) to option eight, where NCAPs incorporate a distraction rating in the overall vehicle safety rating. The benefits of introducing a highly effective (best case) distraction rating system are estimated to result in a road crash saving of approximately AU$28 per ‘improved/low distraction’ vehicle per year.