A Longitudinal Simulator Study to Explore Drivers' Behaviour in Level 3 Automated Vehicles

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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Modeling take-over performance in level 3 conditionally automated vehicles

Accident Analysis & Prevention ◽

10.1016/j.aap.2017.11.009 ◽

2018 ◽

Vol 116 ◽

pp. 3-13 ◽

Cited By ~ 37

Author(s):

Christian Gold ◽

Riender Happee ◽

Klaus Bengler

Keyword(s):

Automated Vehicles ◽

Level 3

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Human-Centered Intervention Based on Tactical-Level Input in Unscheduled Takeover Scenarios for Highly-Automated Vehicles

International Journal of Intelligent Transportation Systems Research ◽

10.1007/s13177-019-00217-x ◽

2019 ◽

Vol 18 (3) ◽

pp. 451-460

Author(s):

Mitsuhiro Kamezaki ◽

Hiroaki Hayashi ◽

Udara E. Manawadu ◽

Shigeki Sugano

Keyword(s):

Driving Simulator ◽

Situational Awareness ◽

Functional Limitations ◽

Hand Gesture ◽

Haptic Interfaces ◽

Automated Vehicles ◽

Operating Level ◽

Driver Workload ◽

Level 3 ◽

Control Authority

AbstractDue to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

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Communications and Driver Monitoring Aids for Fostering SAE Level-4 Road Vehicles Automation

Electronics ◽

10.3390/electronics7100228 ◽

2018 ◽

Vol 7 (10) ◽

pp. 228 ◽

Cited By ~ 6

Author(s):

Felipe Jiménez ◽

José Naranjo ◽

Sofía Sánchez ◽

Francisco Serradilla ◽

Elisa Pérez ◽

...

Keyword(s):

Decision Making ◽

Cooperative Systems ◽

Automated Vehicles ◽

Automated Driving ◽

Road Vehicles ◽

Driver Monitoring ◽

Level 3 ◽

Assistance Systems ◽

Level 2

Road vehicles include more and more assistance systems that perform tasks to facilitate driving and make it safer and more efficient. However, the automated vehicles currently on the market do not exceed SAE level 2 and only in some cases reach level 3. Nevertheless, the qualitative and technological leap needed to reach level 4 is significant and numerous uncertainties remain. In this sense, a greater knowledge of the environment is needed for better decision making and the role of the driver changes substantially. This paper proposes the combination of cooperative systems with automated driving to offer a wider range of information to the vehicle than on-board sensors currently provide. This includes the actual deployment of a cooperative corridor on a highway. It also takes into account that in some circumstances or scenarios, pre-set or detected by on-board sensors or previous communications, the vehicle must hand back control to the driver, who may have been performing other tasks completely unrelated to supervising the driving. It is thus necessary to assess the driver’s condition as regards retaking control and to provide assistance for a safe transition.

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Defining A Design Space of The Auto-Mobile Office: A Computational Abstraction Hierarchy Analysis

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641068 ◽

2020 ◽

Vol 64 (1) ◽

pp. 293-297 ◽

Cited By ~ 1

Author(s):

Mengyao Li ◽

Atefeh Katrahmani ◽

Amudha V. Kamaraj ◽

John D. Lee

Keyword(s):

First Principles ◽

Design Space ◽

Computational Approach ◽

Automated Vehicles ◽

Work Related ◽

Commute Time ◽

Hierarchy Analysis ◽

Level 3 ◽

Mobile Office ◽

Computational Representation

One advantage of highly automated vehicles is drivers can use commute time for non-driving tasks, such as work-related tasks. The potential for an auto-mobile office—a space where drivers work in automated vehicles—is a complex yet underexplored idea. This paper begins to define a design space of the auto- mobile office in SAE Level 3 automated vehicles by integrating the affinity diagram (AD) with a computational representation of the abstraction hierarchy (AH). The AD uses a bottom-up approach where researchers starting with individual findings aggregate and abstract those into higher-level concepts. The AH uses a top-down approach where researchers start with first principles to identify means-ends links between system goals and concrete forms of the system. Using the programming language R, the means-ends links of AH can be explored statistically. This computational approach to the AH provides a systematic means to define the design space of the auto-mobile office.

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Development and Verification of Infrastructure-Assisted Automated Driving Functions

Electronics ◽

10.3390/electronics10172161 ◽

2021 ◽

Vol 10 (17) ◽

pp. 2161

Author(s):

Martin Rudigier ◽

Georg Nestlinger ◽

Kailin Tong ◽

Selim Solmaz

Keyword(s):

Driving Safety ◽

Automated Vehicles ◽

Automated Driving ◽

Human Driver ◽

Decision Making Processes ◽

Level 3 ◽

Sensor Information ◽

Vehicle Platoons ◽

Eu Project ◽

The Eu

Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.

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