A Systematic Review and Meta-Analysis of Takeover Performance During Conditionally Automated Driving

2020 ◽  
pp. 001872082097647
Author(s):  
Bradley W. Weaver ◽  
Patricia R. DeLucia
Keyword(s):  
Time Budget ◽  
Meta Analysis ◽  
Task Engagement ◽  
Information Support ◽  
Future Research ◽  
Automated Driving ◽  
Driving System ◽  
Weak Evidence ◽  
Dependent Variables ◽  
Driving Task

Objective The aim of this paper was to synthesize the experimental research on factors that affect takeover performance during conditionally automated driving. Background For conditionally automated driving, the automated driving system (ADS) can handle the entire dynamic driving task but only for limited domains. When the system reaches a limit, the driver is responsible for taking over vehicle control, which may be affected by how much time they are provided to take over, what they were doing prior to the takeover, or the type of information provided to them during the takeover. Method Out of 8446 articles identified by a systematic literature search, 48 articles containing 51 experiments were included in the meta-analysis. Coded independent variables were time budget, non-driving related task engagement and resource demands, and information support during the takeover. Coded dependent variables were takeover timing and quality measures. Results Engaging in non-driving related tasks results in degraded takeover performance, particularly if it has overlapping resource demands with the driving task. Weak evidence suggests takeover performance is impaired with shorter time budgets. Current implementations of information support did not affect takeover performance. Conclusion Future research and implementation should focus on providing the driver more time to take over while automation is active and should further explore information support. Application The results of the current paper indicate the need for the development and deployment of vehicle-to-everything (V2X) services and driver monitoring.

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

scholarly journals The effects of shoe collar height on ankle sprain mechanics in athletes: A review of literature

2020 ◽  
Vol 27 (2) ◽  
pp. 221-230
Author(s):  
Jojo Hoi-Ching Lai ◽  
Samuel KK Ling ◽  
Patrick Cacho ◽  
SW Mok ◽  
Patrick SH Yung
Keyword(s):  
Ankle Sprain ◽  
Muscle Activation ◽  
Literature Search ◽  
Dynamic Testing ◽  
Meta Analysis ◽  
Future Research ◽  
Systematic Literature Search ◽  
Review Of Literature ◽  
Weak Evidence ◽  
Future Direction

Background: Our aim was to conduct a review to summarize the existing information regarding the effects of shoe collar height in altering ankle sprain mechanics in athletes. Methods: A systematic literature search of PubMed, Embase, MEDLINE, and SPORTDiscus was conducted in September 2019. Results: There were 10 studies published from 1993 to 2019 that were included. Most studies showed high-top shoes limited ankle sprain kinematics and increased resistance to inversion moment in static but not dynamic testing. High-top shoes were associated with delayed pre-landing ankle evertor muscle activation and smaller electromyography amplitudes. Conclusions: There is currently weak evidence to support that high-top shoes can limit ankle sprain kinematics in dynamic testing. Further studies with more consistent study interventions and outcome variables are needed to definitively establish the effects of shoe collar height on ankle sprain mechanics in athletes. The Translational Potential of this Article: Multiple studies on the effects of shoe collar height and ankle sprain mechanics have been performed but there is a lack of consistency in terms of study design, intervention, and outcome measures. A formal systematic review and meta-analysis were not applicable due to the heterogeneity of studies, and mixed results from these studies can be confusing to interpret, making further research on this topic difficult as a result of lack of future direction. We summarized the existing literature on this topic to provide a clearer picture and guide future research on this controversial matter.

scholarly journals A Taxonomy of Strategies For Supporting Time-Sharing With Non-Driving Tasks in Automated Driving

2019 ◽  
Vol 63 (1) ◽  
pp. 2088-2092
Author(s):  
Dengbo He ◽  
Dina Kanaan ◽  
Birsen Donmez
Keyword(s):  
Driver Distraction ◽  
Mitigation Strategies ◽  
Future Research ◽  
Time Sharing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Negative Effects ◽  
Spare Capacity ◽  
Control Automation ◽  
Driving Task

Driver distraction is one of the leading causes of vehicle crashes. The introduction of higher levels of vehicle control automation is expected to alleviate the negative effects of distraction by delegating the driving task to automation, thus enabling drivers to engage in non-driving-related tasks more safely. However, before fully automated vehicles are realized, drivers are still expected to play a supervisory role and intervene with the driving task if necessary while potentially having more spare capacity for engaging in non-driving-related tasks. Traditional distraction mitigation perspectives need to be shifted for automated vehicles from mainly preventing the occurrence of non-driving-related tasks to dynamically coordinating time-sharing between driving and non-driving-related tasks. In this paper, we provide a revised and expanded taxonomy of driver distraction mitigation strategies, discuss how the different strategies can be used in an automated driving context, and propose directions for future research in supporting time-sharing in automated vehicles.

scholarly journals Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sónia Soares ◽  
António Lobo ◽  
Sara Ferreira ◽  
Liliana Cunha ◽  
António Couto
Keyword(s):  
Systematic Review ◽  
Driving Simulator ◽  
Time Budget ◽  
Meta Analysis ◽  
Road Transport ◽  
Driving Simulation ◽  
Automated Driving ◽  
Experimental Conditions ◽  
Secondary Tasks ◽  
Crash Rates

Abstract Introduction In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention.

scholarly journals Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260953
Author(s):  
Sina Nordhoff ◽  
Jork Stapel ◽  
Xiaolin He ◽  
Alexandre Gentner ◽  
Riender Happee
Keyword(s):  
Structural Equation ◽  
Task Engagement ◽  
Equation Modeling ◽  
Future Research ◽  
Automated Driving ◽  
Online Questionnaire ◽  
Perceived Safety ◽  
Driving Mode ◽  
Level 2 ◽  
Partial Automation

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

Path-Goal Theories of Leadership: A Meta-Analysis

1993 ◽  
Vol 19 (4) ◽  
pp. 857-876 ◽  
Author(s):  
J. C. Wofford ◽  
Laurie Z. Liska
Keyword(s):  
Opposite Direction ◽  
Meta Analysis ◽  
Future Research ◽  
Leader Behaviors ◽  
Chi Square ◽  
Dependent Variables ◽  
Meta Analyses ◽  
Theories Of Leadership

Meta-analyses of 120 studies were conducted to test hypotheses of path-goal theories. Chi-square results showed that potential situational and artifactual moderators exist for the relationships of leader behaviors with the dependent variables. Of 16 moderator tests that could be conducted, 7 met the criteria as moderators; however the effect of one moderator was in the opposite direction to that hypothesized. The analyses indicated that much of the research testing path-goal theories has been flawed. Suggestions for future research are made.

scholarly journals A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

2022 ◽  
Vol 11 (1) ◽  
pp. 1-33
Author(s):  
Alexander Diel ◽  
Sarah Weigelt ◽  
Karl F. Macdorman
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Future Research ◽  
Analysis Model ◽  
Moderator Variable ◽  
Methodological Foundation ◽  
Indirect Measures ◽  
Dependent Variables ◽  
Human Likeness ◽  
Independent Variable

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity , and eeriness , and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance , and viewing duration . This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research.

Sign in / Sign up

Export Citation Format

Share Document