Effects of human-machine interaction mechanisms on situation awareness in partly automated driving

Author(s):  
Felix Wulf ◽  
Kathrin Zeeb ◽  
Maria Rimini-Doring ◽  
Marc Arnon ◽  
Frank Gauterin
Author(s):  
Johannes Kraus ◽  
David Scholz ◽  
Dina Stiegemeier ◽  
Martin Baumann

Objective This paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles. Background The positive outcomes of automation can only reach their full potential if a calibrated level of trust is achieved. In this process, information on system capabilities and limitations plays a crucial role. Method In two simulator experiments, trust was repeatedly measured during an automated drive. In Study 1, all participants in a two-group experiment experienced a system-initiated take-over, and the occurrence of a system malfunction was manipulated. In Study 2 in a 2 × 2 between-subject design, system transparency was manipulated as an additional factor. Results Trust was found to increase during the first interactions progressively. In Study 1, take-overs led to a temporary decrease in trust, as did malfunctions in both studies. Interestingly, trust was reestablished in the course of interaction for take-overs and malfunctions. In Study 2, the high transparency condition did not show a temporary decline in trust after a malfunction. Conclusion Trust is calibrated along provided information prior to and during the initial drive with an automated vehicle. The experience of take-overs and malfunctions leads to a temporary decline in trust that was recovered in the course of error-free interaction. The temporary decrease can be prevented by providing transparent information prior to system interaction. Application Transparency, also about potential limitations of the system, plays an important role in this process and should be considered in the design of tutorials and human-machine interaction (HMI) concepts of automated vehicles.


2021 ◽  
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro

Automated driving is becoming a reality, such technology raises new concerns about human-machine interaction on-road. Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs Distrustful) on drivers’ behaviors and trust calibration during simulated Highly Automated Driving (HAD). The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results showed an increase of trust in automation over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.


Author(s):  
Heikki Mansikka ◽  
Kai Virtanen ◽  
Don Harris ◽  
Jaakko Salomäki

This paper advances live (L), virtual (V), and constructive (C) simulation methodologies by introducing a new LVC simulation framework for the development of air combat tactics, techniques, and procedures (TTP). In the framework, TTP is developed iteratively in separate C-, V-, and L-simulation stages. This allows the utilization of the strengths of each simulation class while avoiding the challenges of pure LVC simulations. The C-stage provides the optimal TTP with respect to the probabilities of survival ( Ps) and kill ( Pk) of aircraft without considering the human–machine interaction (HMI). In the V-stage, the optimal TTP is modified by assessing its applicability with Pk and Ps, as well as HMI measures regarding pilots’ situation awareness, mental workload, and TTP adherence. In the L-stage, real aircraft are used to evaluate whether the developed TTP leads to acceptable Pk, Ps, and HMI measures in a real-life environment. The iterative nature of the framework enables that V- or L-stages can reveal flaws of the TTP and an inadequate TTP can be returned to the C- or V-stage for revision. This paper is Part 1 of a two-part study. Part 2 demonstrates the use of the framework with operationally used C- and V-simulators as well as real F/A-18C aircraft and pilots.


Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro

Objective Automated driving is becoming a reality, and such technology raises new concerns about human–machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time. Background Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context. Method Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs. Distrustful) on drivers’ behaviors and trust calibration during two sessions of simulated automated driving. The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human–machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results Results showed an increase of trust over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style. Conclusion Trust in automated driving increases rapidly when drivers’ experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers’ mental model about automated driving systems. Application In the automated driving context, trust calibration is a decisive question to guide such systems’ proper utilization, and road safety.


Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 410
Author(s):  
Yannick Forster ◽  
Frederik Naujoks ◽  
Andreas Keinath

Empirical validation and verification procedures require the sophisticated development of research methodology. Therefore, researchers and practitioners in human–machine interaction and the automotive domain have developed standardized test protocols for user studies. These protocols are used to evaluate human–machine interfaces (HMI) for driver distraction or automated driving. A system or HMI is validated in regard to certain criteria that it can either pass or fail. One important aspect is the number of participants to include in the study and the respective number of potential failures concerning the pass/fail criteria of the test protocol. By applying binomial tests, the present work provides recommendations on how many participants should be included in a user study. It sheds light on the degree to which inferences from a sample with specific pass/fail ratios to a population is permitted. The calculations take into account different sample sizes and different numbers of observations within a sample that fail the criterion of interest. The analyses show that required sample sizes increase to high numbers with a rising degree of controllability that is assumed for a population. The required sample sizes for a specific controllability verification (e.g., 85%) also increase if there are observed cases of fails in regard to the safety criteria. In conclusion, the present work outlines potential sample sizes and valid inferences about populations and the number of observed failures in a user study.


Author(s):  
Lucas Paletta

AbstractHuman attention processes play a major role in the optimization of human-machine interaction (HMI) systems. This work describes a suite of innovative components within a novel framework in order to assess the human factors state of the human operator primarily by gaze and in real-time. The objective is to derive parameters that determine information about situation awareness of the human collaborator that represents a central concept in the evaluation of interaction strategies in collaboration. The human control of attention provides measures of executive functions that enable to characterize key features in the domain of human-machine collaboration. This work presents a suite of human factors analysis components (the Human Factors Toolbox) and its application in the assembly processes of a future production line. Comprehensive experiments on HMI are described which were conducted with typical tasks including collaborative pick-and-place in a lab based prototypical manufacturing environment.


Author(s):  
Heikki Mansikka ◽  
Kai Virtanen ◽  
Don Harris ◽  
Jaakko Salomäki

In this paper, the use of the live (L), virtual (V), and constructive (C) simulation framework introduced in Part 1 of this two-part study is demonstrated in the testing and evaluation of air combat tactics, techniques, and procedures (TTP). Each TTP consists of rules that describe how aircraft pilots coordinate their actions to achieve goals in air combat. In the demonstration, the initial rules are defined by subject matter experts (SMEs). These rules are refined iteratively in separate C-, V-, and L-simulation stages. In the C-stage, an operationally used C-simulation model is used to provide optimal rules with respect to the probabilities of survival ( Ps) and kill ( Pk) of aircraft without considering human–machine interaction (HMI). In the V-stage, fighter squadrons’ V-simulators and SMEs’ assessment are used to modify these rules by evaluating their applicability with Pk and Ps, as well as HMI measures regarding pilots’ situation awareness, mental workload, and TTP rule adherence. In the L-stage, qualified fighter pilots fly F/A-18C aircraft in a real-life environment. Based on SMEs’ assessment, the TTP rules refined in the C- and L-stages result in acceptable Pk, Ps, and HMI measures in the L-stage. As such, the demonstration highlights the utility of the LVC framework.


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