scholarly journals From Interaction to Cooperation: a new approach for human-machine interaction research for closing the out-of-the-loop unfamiliarity

2020 ◽  
Author(s):  
Maximilian Alexander Wächter ◽  
Farbod Nosrat Nezami. ◽  
Nora Maleki ◽  
Philipp Spaniol ◽  
Lea Maria Kühne ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Future Research ◽  
Close Monitoring ◽  
Visual Aids ◽  
Human Machine Interaction ◽  
Out Of The Loop ◽  
Human Driver ◽  
Access Cost ◽  
Cost Efficient ◽  
Cooperation Partners

With the introduction of autonomous vehicles, drivers will be able to engage in non-related tasks while being driven. But in critical situations the car needs the support of the human driver. How do distracted drivers get back into the control-loop quickly when the car requests a take-over? To investigate effective take-over actions, we developed an interactive virtual reality experiment, that uses premises of the embodied cognition theory. Accordingly, the car should not only provide sensory input, but also help enhance the driver’s motor response by interpreting intention and thus helping to accomplish desired actions. This binds humans and machines together in becoming true cooperation partners in joint action. Therefore, we aim for a close monitoring of participants combined with sensorimotor feedforward and feedback. The presented prototype also serves as an open-access, cost-efficient toolkit that enables interested researchers to tailor the presented LoopAR tool to their own needs as part of a previously published toolkit called WestDrive. With the presented work, we hope to shift the paradigm of future research from only visual aids to full sensorimotor integration assistance.

scholarly journals Westdrive X LoopAR: An Open-Access Virtual Reality Project in Unity for Evaluating User Interaction Methods during Takeover Requests

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1879 ◽  
Author(s):  
Farbod N. Nezami ◽  
Maximilian A. Wächter ◽  
Nora Maleki ◽  
Philipp Spaniol ◽  
Lea M. Kühne ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Open Access ◽  
Force Feedback ◽  
User Interaction ◽  
Human Machine Interaction ◽  
Access Cost ◽  
Cost Efficient ◽  
Further Development ◽  
Machine Interaction ◽  
Traffic Behavior

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human–machine interactions, a mobile, versatile, and cost-efficient setup is needed. Here, we describe a virtual reality toolkit for the Unity 3D game engine containing all the necessary code and assets to enable fast adaptations to various human–machine interaction experiments, including closely monitoring the subject. The presented project contains all the needed functionalities for realistic traffic behavior, cars, pedestrians, and a large, open-source, scriptable, and modular VR environment. It covers roughly 25 km2, a package of 125 animated pedestrians, and numerous vehicles, including motorbikes, trucks, and cars. It also contains all the needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes all necessary scripts for eye-tracking in the used devices. All the main functions are integrated into the graphical user interface of the Unity® editor or are available as prefab variants to ease the use of the embedded functionalities. This project’s primary purpose is to serve as an open-access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators. To ensure the accessibility and usability of the mentioned toolkit, we performed a user experience report, also included in this paper.

scholarly journals Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4647
Author(s):  
Anh-Tu Nguyen ◽  
Jagat Jyoti Rath ◽  
Chen Lv ◽  
Thierry-Marie Guerra ◽  
Jimmy Lauber
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Shared Control ◽  
Control Technique ◽  
Vehicle Speed ◽  
Time Varying ◽  
Vehicle Model ◽  
Human Machine Interaction ◽  
Human Driver ◽  
Lane Keeping

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

scholarly journals eHMI: Review and Guidelines for Deployment on Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2912
Author(s):  
Juan Carmona ◽  
Carlos Guindel ◽  
Fernando Garcia ◽  
Arturo de la Escalera
Keyword(s):  
Autonomous Vehicles ◽  
Rapid Development ◽  
Autonomous Systems ◽  
Current Status ◽  
Future Research ◽  
Human Machine Interaction ◽  
Broad Perspective ◽  
Recent Developments ◽  
Feedback Data ◽  
Machine Interaction

Human–machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human–machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

The psychological impact of active surveillance in men with prostate cancer: implications for nursing care

2021 ◽  
Vol 30 (10) ◽  
pp. S30-S37
Author(s):  
Sophie Biddle
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Negative Impact ◽  
Psychological Impact ◽  
Future Research ◽  
Management Approach ◽  
Close Monitoring ◽  
Illness Uncertainty ◽  
Quantitative Studies ◽  
Positive Treatment

Introduction: Active surveillance is a conservative management approach to treating prostate cancer involving regular testing and close monitoring by the health professional. The aim of this literature review is to establish whether men experience a psychological impact of active surveillance and what the prevalent effects might be. Method: The search was carried out in three databases: CINAHL, Medline and PsycINFO. Articles published in English, from October 2015 to March 2018, which focused on the psychological impact of active surveillance, were included. Findings: A total of eight quantitative studies were included in this report. The review identified key psychological impacts of active surveillance, including anxiety, sub-clinical depression, illness uncertainty and hopelessness. Active surveillance was seen by some patients as a positive treatment approach that limited the side effects associated with active treatment. Conclusion: The evidence found a negative impact of active surveillance might be felt by men at any stage during treatment and at differing levels of severity. The article highlights key demographic areas, including ethnicity and age, for future research and recommends more qualitative studies are conducted.

scholarly journals OESDs in an on-road study of semi-automated vehicle to human driver handovers

2021 ◽  
Author(s):  
Neville A. Stanton ◽  
James W. Brown ◽  
Kirsten M. A. Revell ◽  
Jisun Kim ◽  
Joy Richardson ◽  
...  
Keyword(s):  
Vehicle Control ◽  
Future Research ◽  
Strong Correlations ◽  
Sequence Diagrams ◽  
Driving Simulators ◽  
Human Driver ◽  
Handover Process ◽  
Vehicle Automation ◽  
The Uk ◽  
Road Study

AbstractDesign of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research.

Challenging human driver taxis with shared autonomous vehicles: a case study of Chicago

2019 ◽  
Vol 12 (10) ◽  
pp. 701-705 ◽  
Author(s):  
Jun Liu ◽  
Steven Jones ◽  
Emmanuel Kofi Adanu
Keyword(s):  
Autonomous Vehicles ◽  
Human Driver

scholarly journals Drivers’ Perceptions of Functionality Implied by Terms Used to Describe Automation in Vehicles

2018 ◽  
Vol 62 (1) ◽  
pp. 1893-1897 ◽  
Author(s):  
Michael A. Nees
Keyword(s):  
Future Research ◽  
Automated Vehicle ◽  
Human Driver ◽  
Trust In Automation ◽  
Vehicle Automation ◽  
First Time

The expectations induced by the labels used to describe vehicle automation are important to understand, because research has shown that expectations can affect trust in automation even before a person uses the system for the first time. An online sample of drivers rated the perceived division of driving responsibilities implied by common terms used to describe automation. Ratings of 13 terms were made on a scale from 1 (“human driver is entirely responsible”) to 7 (“vehicle is entirely responsible”) for three driving tasks (steering, accelerating/braking, and monitoring). In several instances, the functionality implied by automation terms did not match the technical definitions of the terms and/or the actual capabilities of the automated vehicle functions currently described by the terms. These exploratory findings may spur and guide future research on this under-examined topic.

scholarly journals Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

2020 ◽  
Vol 19 (1) ◽  
pp. 85-88
Author(s):  
A. S. J. Cervera ◽  
F. J. Alonso ◽  
F. S. García ◽  
A. D. Alvarez
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Driver Behaviour ◽  
Safety Issues ◽  
Current State ◽  
Surrounding Environment ◽  
Human Driver ◽  
State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

Sign in / Sign up

Export Citation Format

Share Document