How Full Vehicle Automation affects Driving, Under the Influence of Alcohol and Without It

Yisrael Parmet, Lee Shoham and Tal Oron-Gilad

Presentation at the ICTTP 2016.

link to presentation: How full vehicle automation affects…
DESCRIPTION: The purpose of this study was to examine the effects of full vehicle automation on performance and behavior, specifically the transition from a fully automated mode to manual driving, under the influence of alcohol and without it. Previous studies have revealed a deterioration in driving performance while transitioning from an automated mode to manual driving and further suggested that automated driving may result in a degraded situation awareness. It was therefore hypothesized that the performance of secondary driving related tasks would deteriorate during the automated phase, while performance of secondary non-driving related tasks would improve, in comparison to manual driving. It was further hypothesized that the transition from automated to manual driving would damage driving performance and that alcohol, while affecting performance of all driving conditions, would affect the manual phase following the automated phase to a greater extent. Method. A fixed base driving simulator was used. The design contained a first manual phase, an automated phase and another manual phase, under the influence of BAC 0.05% alcohol and without it. The study involved 16 participants. Two type of secondary tasks were introduced to the participants, driving and non-driving related tasks and the precision (% of success) and response time (RT) were measured. Driving quality indices such as speed and lane position were measures along the drive as well. Results. In the nondriving related secondary task we found significant differences in the response time only, the response time under the placebo condition were on average 15% higher than the response time under the alcohol condition. In the driving related secondary task we found significant difference in both measures, the participants on average were 5% more accurate and 13% faster while they drove manually. The results of the driving quality indices indicate a deterioration in precision of driving related secondary tasks, and a decrease in driving velocity after an automated phase, the latter being moderated by alcohol, which causes an increase in driving velocity. Conclusion. As hypothesized the performance of secondary driving related tasks deteriorated during the automated phase but contrary to our hypothesis, the automation had no influence on the performance of the non-driving secondary task. Opposing to our hypothesis, we found no evidence that alcohol deteriorates the drivers’ performance in the two types of secondary tasks. The last results might be due to the low level of alcohol that was used in the experiment. As expected we found that driving quality decreases after automated phase and while performing secondary tasks.

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Is Staying out of Bomb-Shelters a Human-Automation Interaction Issue?

Co-authored with Guy Cohen-Lazry, to appear in Technology in Society.

Is staying out of bomb-shelters a human-automation interaction issue?http://dx.doi.org/10.1016/j.techsoc.2016.08.002

 

Abstract

“Iron-Dome” is an anti-rocket air defense system placed around major urban areas of Israel. It was created to provide citizens with greater deal of protection against hostile rocket attacks. A study was conducted to examine whether civilians’ experience with the “Iron-Dome” system affects people’s perceived reliability of it, their trust in it, and their complacency to hostile rocket alerts. During the 2014 Israel-Gaza conflict (operation “Protective Edge”), an online questionnaire was used to measure civilian respondents’ perceptions and actions. Results indicated that people living in geographical areas who had more experience with rocket attacks and thereby with the “Iron-Dome” system, perceived it as less reliable, had lower trust in it, and were less complacent. These results show that people’s interaction with the “Iron-Dome” corresponds to the common prediction of theoretical models of human-automation interaction. This understanding may assist in planning of implementation programs and guidance of civilians for other mass protection systems in the future.

 Key words: Iron-Dome, Complacency, Trust in automation, Experience, Rocket defense system.

 

 

 

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Can traffic violations be traced to gender-role, sensation seeking, demographics and driving exposure?

A new publication co-authored by Ilit Oppenheim, Yisrael Parmet and David Shinar published in Transportation Research Part F.

http://dx.doi.org/10.1016/j.trf.2016.06.027

ABSTRACT
Background: Traffic safety is often expressed as the ‘inverse of accidents’. However, it is
more than the mere absence of accidents. Past studies often looked for associations
between accidents and self-reports like the Driver Behaviour Questionnaire
(DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). The focus in this study
changed from counting accidents to quantifying unsafe acts as violations. The objective
was to show that drivers’ specific violations can be traced to personal characteristics such
as sensation seeking (SSS-V; Zuckerman, 1994), gender role (BSRI; Bem sex role inventory,
Bem, 1974), demographics, and driving exposure.
Method: A web-based questionnaire was distributed, integrating several known questionnaires.
Five hundred and twenty-seven questionnaires were completed and analyzed.
Results: Sensation seeking, gender role, experience, and age predicted respondents’ score
on the DBQ, as well as the interaction of sensation seeking with gender and gender role.
Gender role was a more valid predictor of driver behavior than gender.
Conclusions: The effect of gender role on drivers’ self-reported violation tendency is the
most interesting and the most intriguing finding of this survey and indicates the need to
further examine gender role affects in driving.

 

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Come see us this summer in HCI 2016

HCI International 2016  17 – 22 July 2016

Shanne Honig will be presenting a poster on an experiment that we conducted as part of our ‘Follow Me’ research: Proxemics and Responsiveness Preferences of Elderly Users in a Person-Following Robot.

follow me

A person-following robotic assistant

 

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Cell phone conversations and child pedestrian’s crossing behavior; a simulator study

This is our most recent publication, accepted for publication in Safety Science.

Please cite this article in press as: Tapiro, H., et al. Cell phone conversations and child pedestrian’s crossing behavior; a simulator study. Safety Sci. (2016), http://dx.doi.org/10.1016/j.ssci.2016.05.013

Cell phone conversations and child pedestrian’s crossing behavior; a simulator study

Hagai Tapiro, Yisrael Parmet and Tal Oron-Gilad

Abstract

Child pedestrians are highly represented in fatal and severe road crashes and differ in their crossing behavior from adults. Although many children carry cell phones, the effect that cell phone conversations have on children’s crossing behavior has not been thoroughly examined. A comparison of children and adult pedestrians’ crossing behavior while engaged in cell phone conversations was conducted. In a semi-immersive virtual environment simulating a typical city, 14 adults and 38 children (11 children aged 7-8; 18 aged 9-10 and 9 aged 11-13), experienced road crossing related traffic-scene scenarios. They were requested to press a response button whenever they felt it was safe to cross. Eye movements were tracked. Results have shown that all age groups’ crossing behaviors were affected by cell phone conversations. When busy with more cognitively demanding conversation types, participants were slower to react to a crossing opportunity, chose smaller crossing gaps, and allocated less visual attention to the peripheral regions of the scene. The ability to make better crossing decisions improved with age, but no interaction with cell phone conversation type was found. The most prominent improvement was shown in ‘safety gap’; each age group maintained a longer gap than its predecessor younger age group. In accordance to the current study, it is safe to say that cell phone conversations can hinder child and adult pedestrians’ safety. Thereby, it is important to take those findings in account when aiming to train young pedestrians for road-safety and increase public awareness.

Interested in seeing an interactive visualization app of our data?https://eyemove.shinyapps.io/cell-phone/

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Interactive app to view the eye gaze data. Click on the link and follow the instructions shown above.

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IEEE RO-MAN 2016 upcoming presentations

Two of our works have been accepted as full papers for presentation and publication in the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2016).

“Postures of a Robot Arm – window to robot intentions?” authored by my doctoral student Sridatta Chaterjee and co-authored by my colleagues Drs. Oren Shriki and Idit Shalev.

Abstract— Body language of robot arms, have rarely been explored as a medium of conveying robot intentions. An exploratory study was done focusing on two questions: one, if robot arm postures can convey robot intentions, and two, if participants coming in contact with this robot arm for the first time can associate any meaning to the postures without watching the robot in action, or working with it. Thirty five participants of a wide age range (25-70) took part in this exploratory study. Results show that participants could interpret some postures. Four distinct types of postures have been selected to four separate categories by the majority of participants irrespective of their age. In addition, postures selected in categories like, ‘Robot giving object in a friendly manner’; ‘Robot is saying Hi!’, ‘Robot has been told not to disturb’ show similarity to body language exhibited by humans and animals while communicating such messages.

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Posture 8, what is the robot doing?

“The Influence of Following Angle on Performance Metrics of a Human-Following Robot” co-authored by our graduate students Shanee Honig and Dror Katz, and my colleague Prof. Yael Edan.

Abstract— Robots that operate alongside people need to be able to move in socially acceptable ways. As a step toward this goal, we study how and under which circumstances the angle at which a robot follows a person may affect the human experience and robot tracking performance. In this paper, we aimed to assess three following angles (0◦ angle, 30◦ angle, and 60◦ angle) under two conditions: when the robot was carrying a valuable personal item or not. Objective and subjective indicators of the quality of following and participants’ perceptions and preferences were collected. Results indicated that the personal item manipulation increased awareness to the quality of the following and the following angles. Without the manipulation, participants were indifferent to the behavior of the robot. Our following algorithm was successful for tracking at a 0◦ and 30◦ angle, yet it must be improved for wider angles. Further research is required to obtain better understanding of following angle preferences for varying environment and task conditions.

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Following angles of a person-following robot: straight from behind or wider angles?

NY, Looking forward to two great presentations!


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Following Angle of a Human-Following Robot

Human-following capabilities of robots may become important in assistive robotic applications to facilitate many daily tasks (e.g. carrying personal items or groceries). Robot’s following distance, following angle and acceleration influence the quality of the interaction between the human and the robot by impacting walking efficiency (e.g., pace, flow and unwanted stops), user comfort and robot likability.

ICR Our team gave a presentation at the ICR 2016 conference focusing on Subjective preferences regarding human-following robots: preliminary evidence from laboratory experiments.

The Influence of Following Angle on Performance Metrics -

Following Angles of a human-following Pioneer LX Robot (Honig, Katz, Edan & Oron-Gilad)

  • This research effort is led by our graduate student Shanee Honig
  • For the person-tracking and following algorithm (Dror Katz & Yael Edan, work in progress) we use the Pioneer LX Robot’s built in camera and a Microsoft Kinect.
  • Currently we focus on 3 angles of following: back following (0 degree angle), a 30 degree angle, and a 60 degree angle.
  • We use a personal item manipulation (e.g., wallet) to examine how participants engage with the robot. Naturally when participants place a personal item on the robot, they become more engaged with it.
  • Come see us at the HCII 2016 where we will present a poster on sensitivity of older users (68 and above) to the quality of interaction, depending on robot’s following distance and acceleration, and the context of walk – Follow Me: Proxemics and Responsiveness Preferences of Older Users in a Human-Following Robot.

 

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