scholarly journals Towards standardized metrics for measuring takeover performance in conditionally automated driving: A systematic review

2021 ◽  
Vol 65 (1) ◽  
pp. 1065-1069
Author(s):  
Yining Cao ◽  
Feng Zhou ◽  
Elizabeth M. Pulver ◽  
Lisa J. Molnar ◽  
Lionel P. Robert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Performance Metrics ◽  
Empirical Literature ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Wide Range ◽  
Level 3

A particular concern with SAE Level 3 automated vehicles is the takeover transition from the automated vehicle to the driver. Prior research has employed a wide range of metrics for measuring takeover performance. However, the lack of a set of standard metrics for measuring takeover performance makes it difficult to consolidate findings and summarize the influence of different factors. This article presents a review of the metrics employed in empirical literature examining takeover transitions in Level 3 automated driving and proposes a framework for standardizing the objective takeover performance metrics.

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 Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals Modeling Cross-National Differences in Automated Vehicle Acceptance

2020 ◽  
Vol 12 (22) ◽  
pp. 9765
Author(s):  
Shelly Etzioni ◽  
Jamil Hamadneh ◽  
Arnór B. Elvarsson ◽  
Domokos Esztergár-Kiss ◽  
Milena Djukanovic ◽  
...  
Keyword(s):  
Mode Choice ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Type Model ◽  
Model Parameters ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
National Differences ◽  
Private Car

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time

2018 ◽  
Vol 60 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Safety Concern ◽  
Reaction Times ◽  
Vigilance Task ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Hazard Detection ◽  
And Performance

Objective: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Background: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Method: Participants “drove” a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. Results: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Conclusion: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. Application: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

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

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 228 ◽  
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.

scholarly journals Development and Verification of Infrastructure-Assisted Automated Driving Functions

Electronics ◽  
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.

Takeover Request Design in Automated Driving: A Systematic Review

2021 ◽  
Vol 65 (1) ◽  
pp. 868-872
Author(s):  
Kevin Joel Salubre ◽  
Dan Nathan-Roberts
Keyword(s):  
Systematic Review ◽  
Current Literature ◽  
Autonomous Vehicles ◽  
Manual Control ◽  
Automated Driving ◽  
Lateral Control ◽  
Vehicle System ◽  
Level 3

Autonomous vehicles (AV) with “level 3” automation and above are expected to take full longitudinal and lateral control, which relinquishes the driver from manual control and allows for engagement with non-driving-related tasks. Despite the advance nature of a level 3 vehicle, system limitations can occur, and the driver is expected to re-engage in manual driving at a moment’s notice. Current literature has been focused on takeover performance during a takeover request (TOR) and the effects of multimodal warnings, but there is little consensus on how modality stimulus is presented. This systematic review summarizes the current designs and implementations of TORs of level 3 AVs and above. Identified themes in the review were categorized into three sections: non-driving-related tasks, driving scenarios, and takeover modality. A summary of how researchers utilized these themes in the current literature are discussed as well as implications and further research.

Peripheral Monitoring of Traffic in Conditionally Automated Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1828-1832 ◽  
Author(s):  
Jonas Radlmayr ◽  
Karin Brüch ◽  
Kathrin Schmidt ◽  
Christine Solbeck ◽  
Tristan Wehner
Keyword(s):  
Situation Awareness ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Active System ◽  
Subject Design ◽  
Level 3 ◽  
Driving Task

Conditionally automated vehicles (level 3) allow drivers to engage in visual, non-driving related tasks (NDRTs) while the automation is active. System limits require drivers to reengage in the dynamic driving task in take-over situations. If the NDRT is visually engaging, situation awareness (SA) necessary for a successful take-over can decrease. This study analyzed, if the SA of drivers increases while monitoring the surrounding traffic peripherally. A semi-transparent balloon game in the head-up display operationalized the engagement into a visual NDRT with the possibility of peripheral monitoring. In addition, participants without the possibility of monitoring due to simulated heavy fog (second group) were tested along with a third group that could monitor surroundings self-determined without a NDRT. The between-subject design included 57 participants. Results showed that self-determined monitoring leads to higher situation awareness compared to peripheral monitoring and no monitoring. This did not result in better take-over performances.

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

scholarly journals Designing Automated Vehicle Interiors for Different Cultures: Evidence From China, Germany, and the United States

2020 ◽  
pp. 106480462096615
Author(s):  
Vanessa Sauer ◽  
Alexander Mertens ◽  
Stefan Groß ◽  
Jens Heitland ◽  
Verena Nitsch
Keyword(s):  
United States ◽  
Well Being ◽  
The United States ◽  
Vehicle Design ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Global Trend ◽  
Design Requirements ◽  
Different Cultures

The advent of automated driving is a global trend. It is likely that views on what will make an automated vehicle trustworthy, comfortable, usable, and enhance passengers’ well-being while driving will differ between markets. Therefore, we conducted an expert survey ( n = 28) to identify cultural-specific design requirements of Level 4 automated vehicles for China, Germany, and the United States. Our results indicate a tendency toward hedonic vehicle design in China and pragmatic design in Germany. United States lies between these two markets. The results imply that car manufacturers can influence passengers’ well-being through vehicle design and, in turn, increase acceptance of automated vehicles.

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