scholarly journals Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

2019 ◽  
Vol 63 (1) ◽  
pp. 287-291
Author(s):  
Huiping Zhou ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Driving Simulator ◽  
Mode Transition ◽  
Automated Driving ◽  
Automated Control ◽  
Human Driver ◽  
Level 3 ◽  
Level Transition ◽  
Verbal Messages ◽  
Level 2 ◽  
Highly Automated Driving

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

scholarly journals Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

2019 ◽  
Vol 3 (2) ◽  
pp. 29 ◽  
Author(s):  
Yannick Forster ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Josef Krems ◽  
Andreas Keinath
Keyword(s):  
Driving Simulator ◽  
Ease Of Use ◽  
Use Case ◽  
Perceived Ease Of Use ◽  
Automated Driving ◽  
Human Machine Interfaces ◽  
Level 3 ◽  
Level 2 ◽  
Over Time

The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.

scholarly journals Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 114 ◽  
Author(s):  
Barbara Metz ◽  
Johanna Wörle ◽  
Michael Hanig ◽  
Marcus Schmitt ◽  
Aaron Lutz
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Driving Function ◽  
Behavioural Adaptation ◽  
The Road ◽  
Driving Time ◽  
Level 3 ◽  
Simulator Study ◽  
First Contact ◽  
Highly Automated Driving

Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.

scholarly journals Driver seat comfort for level 3-4 autonomous vehicles

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S111-S118
Author(s):  
Neil J. Mansfield ◽  
Kartikeya Walia ◽  
Aditya Singh
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Case Methods ◽  
Driving Mode ◽  
Full Control ◽  
Seat Comfort ◽  
Autonomous Mode ◽  
Different Types ◽  
Level 3 ◽  
Level 2

BACKGROUND: Autonomous vehicles can be classified on a scale of automation from 0 to 5, where level 0 corresponds to vehicles that have no automation to level 5 where the vehicle is fully autonomous and it is not possible for the human occupant to take control. At level 2, the driver needs to retain attention as they are in control of at least some systems. Level 3-4 vehicles are capable of full control but the human occupant might be required to, or desire to, intervene in some circumstances. This means that there could be extended periods of time where the driver is relaxed, but other periods of time when they need to drive. OBJECTIVE: The seat must therefore be designed to be comfortable in at least two different types of use case. METHODS: This driving simulator study compares the comfort experienced in a seat from a production hybrid vehicle whilst being used in a manual driving mode and in autonomous mode for a range of postures. RESULTS: It highlights how discomfort is worse for cases where the posture is non-optimal for the task. It also investigates the design of head and neckrests to mitigate neck discomfort, and shows that a well-designed neckrest is beneficial for drivers in autonomous mode.

scholarly journals Mode Awareness and Automated Driving—What Is It and How Can It Be Measured?

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 277 ◽  
Author(s):  
Christina Kurpiers ◽  
Bianca Biebl ◽  
Julia Mejia Hernandez ◽  
Florian Raisch
Keyword(s):  
Gold Standard ◽  
Automated Driving ◽  
Active System ◽  
Operational Design ◽  
Automation Systems ◽  
Study Results ◽  
Level 3 ◽  
No Gold Standard ◽  
Level 2 ◽  
Initial Instruction

In SAE (Society of Automotive Engineers) Level 2, the driver has to monitor the traffic situation and system performance at all times, whereas the system assumes responsibility within a certain operational design domain in SAE Level 3. The different responsibility allocation in these automation modes requires the driver to always be aware of the currently active system and its limits to ensure a safe drive. For that reason, current research focuses on identifying factors that might promote mode awareness. There is, however, no gold standard for measuring mode awareness and different approaches are used to assess this highly complex construct. This circumstance complicates the comparability and validity of study results. We thus propose a measurement method that combines the knowledge and the behavior pillar of mode awareness. The latter is represented by the relational attention ratio in manual, Level 2 and Level 3 driving as well as the controllability of a system limit in Level 2. The knowledge aspect of mode awareness is operationalized by a questionnaire on the mental model for the automation systems after an initial instruction as well as an extensive enquiry following the driving sequence. Further assessments of system trust, engagement in non-driving related tasks and subjective mode awareness are proposed.

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.

Comfort Assessment for Highly Automated Driving Functions at the Stuttgart Driving Simulator

2021 ◽  
pp. 368-379
Author(s):  
Christoph Heimsath ◽  
Werner Krantz ◽  
Jens Neubeck ◽  
Christian Holzapfel ◽  
Andreas Wagner
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Highly Automated Driving

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.

scholarly journals Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Safety ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Niklas Grabbe ◽  
Alain Gales ◽  
Michael Höcher ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Driving Simulator ◽  
Road Traffic ◽  
Structured Interviews ◽  
Automated Driving ◽  
Design Recommendations ◽  
Resonance Analysis ◽  
Performance Variability ◽  
Human Driver

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.

Sign in / Sign up

Export Citation Format

Share Document