Human Driver’s Acceptance of Automated Driving Systems Based on a Driving Simulator Study

2020 ◽  
pp. 186-195
Author(s):  
Georg Hanzl ◽  
Michael Haberl ◽  
Arno Eichberger ◽  
Martin Fellendorf
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Simulator Study

Function-Specific Uncertainty Communication in Automated Driving

2019 ◽  
Vol 11 (2) ◽  
pp. 75-97
Author(s):  
Alexander Kunze ◽  
Stephen J. Summerskill ◽  
Russell Marshall ◽  
Ashleigh J. Filtness
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Driving Experience ◽  
Visual Variables ◽  
Simulator Study ◽  
The Impact

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

2021 ◽  
Vol 162 ◽  
pp. 106408
Author(s):  
Klemens Weigl ◽  
Clemens Schartmüller ◽  
Philipp Wintersberger ◽  
Marco Steinhauser ◽  
Andreas Riener
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Automated Driving ◽  
Simulator Study

Function-Specific Uncertainty Communication in Automated Driving

2022 ◽  
pp. 1002-1026
Author(s):  
Alexander Kunze ◽  
Stephen J. Summerskill ◽  
Russell Marshall ◽  
Ashleigh J. Filtness
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Driving Experience ◽  
Visual Variables ◽  
Simulator Study ◽  
The Impact

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

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

2020 ◽  
Vol 32 (3) ◽  
pp. 520-529
Author(s):  
Keisuke Suzuki ◽  
◽  
Joohyeong Lee ◽  
Atsushi Kanbe
Keyword(s):  
Traffic Accidents ◽  
State Transition ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Automated Driving ◽  
System Safety ◽  
Safety Level ◽  
Simulator Study ◽  
Level 2 ◽  
Integrated Error

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.

scholarly journals How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

2019 ◽  
Vol 62 (7) ◽  
pp. 1212-1229
Author(s):  
Giulio Bianchi Piccinini ◽  
Esko Lehtonen ◽  
Fabio Forcolin ◽  
Johan Engström ◽  
Deike Albers ◽  
...  
Keyword(s):  
Prediction Model ◽  
Computational Models ◽  
Driving Simulator ◽  
Reaction Times ◽  
Automated System ◽  
Cruise Control ◽  
Automated Driving ◽  
Driver Response ◽  
Driving Condition ◽  
Simulator Study

Objective This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

scholarly journals How the Initial Level of Trust in Automated Driving Impacts Drivers’ Behaviour and Early Trust Construction

2021 ◽  
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro
Keyword(s):  
Driving Simulator ◽  
Initial Level ◽  
Environment Monitoring ◽  
Automated Driving ◽  
The Road ◽  
Trust In Automation ◽  
Gaze Behaviour ◽  
Simulator Study ◽  
The Impact ◽  
Highly Automated Driving

Trust in Automation is known to influence human-automation interaction and user behaviour. In the Automated Driving (AD) context, studies showed the impact of drivers’ Trust in Automated Driving (TiAD), and linked it with, e.g., difference in environment monitoring or driver’s behaviour. This study investigated the influence of driver’s initial level of TiAD on driver’s behaviour and early trust construction during Highly Automated Driving (HAD). Forty drivers participated in a driving simulator study. Based on a trust questionnaire, participants were divided in two groups according to their initial level of TiAD: high (Trustful) vs. low (Distrustful). Declared level of trust, gaze behaviour and Non-Driving-Related Activities (NDRA) engagement were compared between the two groups over time. Results showed that Trustful drivers engaged more in NDRA and spent less time monitoring the road compared to Distrustful drivers. However, an increase in trust was observed in both groups. These results suggest that initial level of TiAD impact drivers’ behaviour and further trust evolution.

scholarly journals Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

2019 ◽  
Vol 20 (sup1) ◽  
pp. S152-S156 ◽  
Author(s):  
Sayaka Ono ◽  
Haruto Sasaki ◽  
Hitoshi Kumon ◽  
Yoshitaka Fuwamoto ◽  
Shunsuke Kondo ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Simulator Study

scholarly journals Engagement in Non-Driving Related Tasks as a Non-Intrusive Measure for Mode Awareness: A Simulator Study

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 239 ◽  
Author(s):  
Yannick Forster ◽  
Viktoria Geisel ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Andreas Keinath
Keyword(s):  
Driving Simulator ◽  
Minimum Degree ◽  
Block Design ◽  
Automated System ◽  
Automated Driving ◽  
Repeated Interaction ◽  
Wide Range ◽  
Machine Interface ◽  
Simulator Study ◽  
Design And Practice

Research on the role of non-driving related tasks (NDRT) in the area of automated driving is indispensable. At the same time, the construct mode awareness has received considerable interest in regard to human–machine interface (HMI) evaluation. Based on the expectation that HMI design and practice with different levels of driving automation influence NDRT engagement, a driving simulator study was conducted. In a 2 × 5 (automation level x block) design, N = 49 participants completed several transitions of control. They were told that they could engage in an NDRT if they felt safe and comfortable to do so. The NDRT was the Surrogate Reference Task (SuRT) as a representative of a wide range of visual–manual NDRTs. Engagement (i.e., number of inputs on the NDRT interface) was assessed at the onset of a respective episode of automated driving (i.e., after transition) and during ongoing automation (i.e., before subsequent transition). Results revealed that over time, NDRT engagement increased during both L2 and L3 automation until stable engagement at the third block. This trend was observed for both onset and ongoing NDRT engagement. The overall engagement level and the increase in engagement are significantly stronger for L3 automation compared to L2 automation. These results outline the potential of NDRT engagement as an online non-intrusive measure for mode awareness. Moreover, repeated interaction is necessary until users are familiar with the automated system and its HMI to engage in NDRTs. These results provide researchers and practitioners with indications about users’ minimum degree of familiarity with driving automation and HMIs for mode awareness testing.

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