scholarly journals Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

2018 ◽  
Vol 2 (4) ◽  
pp. 68 ◽  
Author(s):  
Natalie T. Richardson ◽  
Lukas Flohr ◽  
Britta Michel
Keyword(s):  
Driving Simulator ◽  
Situational Awareness ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Transport Efficiency ◽  
Additional Information ◽  
Level 3 ◽  
Driving Task ◽  
Machine Interface ◽  
Vehicle Automation

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

scholarly journals Does a Confidence Level for Automated Driving Time Estimations Improve the Subjective Evaluation of an Automation HMI?

2020 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Tobias Hecht ◽  
Simon Danner ◽  
Alexander Feierle ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Subjective Evaluation ◽  
Mixed Design ◽  
Automated Driving ◽  
Traffic Conditions ◽  
Driving Time ◽  
Level 3 ◽  
Machine Interface ◽  
The Impact ◽  
The City

Current research in human factors and automated driving is increasingly focusing on predictable transitions instead of urgent and critical take-overs. Predictive human–machine interface (HMI) elements displaying the remaining time until the next request to intervene were identified as a user need, especially when the user is engaging in non-driving related activities (NDRA). However, these estimations are prone to errors due to changing traffic conditions and updated map-based information. Thus, we investigated a confidence display for Level 3 automated driving time estimations. Based on a preliminary study, a confidence display resembling a mobile phone connectivity symbol was developed. In a mixed-design driving simulator study with 32 participants, we assessed the impact of the confidence display concept (within factor) on usability, frustration, trust and acceptance during city and highway automated driving (between factor). During automated driving sections, participants engaged in a naturalistic visual NDRA to create a realistic scenario. Significant effects were found for the scenario: participants in the city experienced higher levels of frustration. However, the confidence display has no significant impact on the subjective evaluation and most participants preferred the baseline HMI without a confidence symbol.

scholarly journals Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority

2020 ◽  
pp. 001872082094618 ◽  
Author(s):  
Davide Maggi ◽  
Richard Romano ◽  
Oliver Carsten
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Safety Issues ◽  
The Road ◽  
Operational Design ◽  
Level 3 ◽  
Driving Task

Objective A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control). Background During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance. Method Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions. Results Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road. Conclusion Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers’ authority was detrimental for both performance and comfort. Application As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

scholarly journals The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 73 ◽  
Author(s):  
Tobias Hecht ◽  
Stefan Kratzert ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Transition Frequency ◽  
Gaze Behavior ◽  
Automated Driving ◽  
Negative Effects ◽  
Level 3 ◽  
Machine Interface ◽  
Time Critical ◽  
New Applications ◽  
Study Questionnaire

Automated driving research as a key topic in the automotive industry is currently undergoing change. Research is shifting from unexpected and time-critical take-over situations to human machine interface (HMI) design for predictable transitions. Furthermore, new applications like automated city driving are getting more attention and the ability to engage in non-driving related activities (NDRA) starting from SAE Level 3 automation poses new questions to HMI design. Moreover, future introduction scenarios and automated capabilities are still unclear. Thus, we designed, executed, and assessed a driving simulator study focusing on the effect of different transition frequencies and a predictive HMI while freely engaging in naturalistic NDRA. In the study with 33 participants, we found transition frequency to have effects on workload and acceptance, as well as a small impact on the usability evaluation of the system. Trust, however, was not affected. The predictive HMI was used and accepted, as can be seen by eye-tracking data and the post-study questionnaire, but could not mitigate the above-mentioned negative effects induced by transition frequency. Most attractive activities were window gazing, chatting, phone use, and reading magazines. Descriptively, window gazing and chatting gained attractiveness when interrupted more often, while reading magazines and playing games were negatively affected by transition rate.

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.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

scholarly journals Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Frederik Naujoks ◽  
Yannick Forster ◽  
Katharina Wiedemann ◽  
Alexandra Neukum
Keyword(s):  
Driving Simulator ◽  
Primary Task ◽  
Automated Driving ◽  
Task Interference ◽  
Driving System ◽  
Lane Changes ◽  
Speech Output ◽  
Driving Time ◽  
Standard Information ◽  
Machine Interface

During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver’s primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visual-auditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

scholarly journals The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 809 ◽  
Author(s):  
Johannes Hiller ◽  
Sami Koskinen ◽  
Riccardo Berta ◽  
Nisrine Osman ◽  
Ben Nagy ◽  
...  
Keyword(s):  
Data Management ◽  
Reference Architecture ◽  
Automated Driving ◽  
Iterative Processing ◽  
Vehicle Owner ◽  
Level 3 ◽  
System Functioning ◽  
Comprehensive Test ◽  
Vehicle Automation ◽  
Set Up

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

scholarly journals Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 62 ◽  
Author(s):  
Alexander Feierle ◽  
Simon Danner ◽  
Sarah Steininger ◽  
Klaus Bengler
Keyword(s):  
Visual Attention ◽  
Information Needs ◽  
Driving Simulator ◽  
Automation System ◽  
Automated Driving ◽  
Navigation Data ◽  
Levels Of Automation ◽  
Machine Interface ◽  
The Individual ◽  
Highly Automated Driving

During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

Attentive User Interfaces to Improve Multitasking and Take-Over Performance in Automated Driving

2019 ◽  
Vol 11 (3) ◽  
pp. 40-58 ◽  
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Andreas Riener
Keyword(s):  
User Interfaces ◽  
Lead Time ◽  
Driving Simulator ◽  
Handheld Devices ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Level 3 ◽  
Traffic Systems ◽  
Consumer Devices ◽  
Positive Effect

Automated vehicles promise engagement in side activities, but demand drivers to resume vehicle control in Take-Over situations. This pattern of alternating tasks thus becomes an issue of sequential multitasking, and it is evident that random interruptions result in a performance drop and are further a source of stress/anxiety. To counteract such drawbacks, this article presents an attention-aware architecture for the integration of consumer devices in level-3/4 vehicles and traffic systems. The proposed solution can increase the lead time for transitions, which is useful to determine suitable timings (e.g., between tasks/subtasks) for interruptions in vehicles. Further, it allows responding to Take-Over-Requests directly on handheld devices in emergencies. Different aspects of the Attentive User Interface (AUI) concept were evaluated in two driving simulator studies. Results, mainly based on Take-Over performance and physiological measurements, confirm the positive effect of AUIs on safety and comfort. Consequently, AUIs should be implemented in future automated vehicles.

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