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.

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 Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

2022 ◽  
Author(s):  
Daofei Li ◽  
Linhui Chen
Keyword(s):  
Motion Sickness ◽  
Driving Simulator ◽  
Ride Comfort ◽  
System Development ◽  
Road Transport ◽  
Automated Vehicles ◽  
Automated Driving ◽  
User Friendliness ◽  
Practical Applications ◽  
Vehicle Movement

<p>Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.</p><p>(This article has been accepted for publication in <i>Ergonomics</i>, published by Taylor & Francis.)</p><br>

A systematic review and meta-analysis of vineyard techniques used to delay ripening.

2021 ◽  
Author(s):  
Pietro Previtali ◽  
Filippo Giorgini ◽  
Randall S. Mullen ◽  
Nick K. Dookozlian ◽  
Kerry L. Wilkinson ◽  
...  
Keyword(s):  
Systematic Review ◽  
Quantitative Data ◽  
Meta Analysis ◽  
Data Driven ◽  
Experimental Conditions ◽  
Factors Affecting ◽  
Apical Bud ◽  
Grape Quality ◽  
Source Limitation ◽  
The Impact

Abstract Several vineyard techniques have been proposed to delay grape maturity in light of the advanced maturation driven by increasingly frequent water and heat stress events that are detrimental to grape quality. These studies differ in terms of their experimental conditions, and in the present work we have attempted to summarize previous observations in a quantitative, data-driven systematic review. A meta-analysis of quantitative data gathered across 43 relevant studies revealed the overall significance of the proposed treatments and evaluated the impact of different experimental conditions on the outcome of antitranspirants, delayed pruning and late source limitation. Antitranspirants were most effective when applied twice and closer to veraison, while di-1-p-menthene increased the ripening delay by about 1°Brix compared to kaolin. Larger ripening delays were achieved with delayed pruning of low-yielding vines or by pruning at later stages of apical bud development. Late defoliation or shoot trimming delayed ripening in high-yielding vines and represent suitable solutions for late-harvested varieties, but became ineffective where the treatment decreased yield. This quantitative meta-analysis of 242 primary observations uncovers factors affecting the efficacy of vineyard practices to delay ripening, which should be carefully considered by grape growers attempting to achieve this outcome.

scholarly journals Evaluating mesenchymal stem cell therapy for sepsis with preclinical meta-analyses prior to initiating a first-in-human trial

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Manoj M Lalu ◽  
Katrina J Sullivan ◽  
Shirley HJ Mei ◽  
David Moher ◽  
Alexander Straus ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Studies ◽  
Meta Analysis ◽  
Systematic Review Methodology ◽  
Experimental Conditions ◽  
Human Trial ◽  
Mesenchymal Stem Cell Therapy ◽  
Threats To Validity ◽  
Meta Analyses

Evaluation of preclinical evidence prior to initiating early-phase clinical studies has typically been performed by selecting individual studies in a non-systematic process that may introduce bias. Thus, in preparation for a first-in-human trial of mesenchymal stromal cells (MSCs) for septic shock, we applied systematic review methodology to evaluate all published preclinical evidence. We identified 20 controlled comparison experiments (980 animals from 18 publications) of in vivo sepsis models. Meta-analysis demonstrated that MSC treatment of preclinical sepsis significantly reduced mortality over a range of experimental conditions (odds ratio 0.27, 95% confidence interval 0.18–0.40, latest timepoint reported for each study). Risk of bias was unclear as few studies described elements such as randomization and no studies included an appropriately calculated sample size. Moreover, the presence of publication bias resulted in a ~30% overestimate of effect and threats to validity limit the strength of our conclusions. This novel prospective application of systematic review methodology serves as a template to evaluate preclinical evidence prior to initiating first-in-human clinical studies.

scholarly journals Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

2019 ◽  
Vol 61 (4) ◽  
pp. 642-688 ◽  
Author(s):  
Anthony D. McDonald ◽  
Hananeh Alambeigi ◽  
Johan Engström ◽  
Gustav Markkula ◽  
Tobias Vogelpohl ◽  
...  
Keyword(s):  
Time Budget ◽  
Empirical Studies ◽  
Vehicle Control ◽  
Influential Factors ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Safety Issues ◽  
Automated Vehicle ◽  
Automated Vehicle Control ◽  
The Impact

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

scholarly journals Biopsychosocial factors associated with non-recovery after a minor transport-related injury: protocol for a systematic review

BMJ Open ◽  
2017 ◽  
Vol 7 (9) ◽  
pp. e016314 ◽  
Author(s):  
Stella Samoborec ◽  
Rasa Ruseckaite ◽  
Lorena Romero ◽  
Sue M Evans
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Grey Literature ◽  
Road Transport ◽  
Primary Data ◽  
Cochrane Central Register ◽  
Post Traumatic Stress ◽  
Factors Associated ◽  
Quantitative And Qualitative Research ◽  
Meta Analyses

IntroductionGlobally, road transport accidents contribute substantially to the number of deaths and also to the burden of disability. Up to 50 million people suffer a transport-related non-fatal injury each year, which often leads to long-term disability. It has been shown that substantial number of people with minor injuries struggle to recover and the reasons are still not well explored.Despite the high prevalence, little is known about the factors hindering recovery following minor traffic-related injuries. The aim of this paper is to present a protocol for the systematic review aiming to understand biopsychosocial factors related to non-recovery and identify current gaps in the literature.Methods and analysisThe review will be conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol guidelines. A search of the electronic databases, MEDLINE, EMBASE, Cochrane Central Register of Controlled trials, will be undertaken, in addition to Google Scholar and grey literature to identify studies in period from 2006 to 2016. Quantitative and qualitative research articles describing and identifying biopsychosocial factors associated with non-recovery and health outcomes such as pain, disability, functional recovery, health-related quality of life, post-traumatic stress disorder, depression, anxiety and return to work will be included. A conceptual framework developed to identify biopsychosocial factors will be applied to assure defined criterion.At present, there is little anticipation for meta-analyses due to the heterogeneity of factors and outcomes assessed. Therefore, a narrative synthesis based on study findings will be conducted.Ethics and disseminationEthical approval is not required as primary data will not be collected. Review results will be published as a part of thesis, peer-reviewed journal and conferences.Trialregistration numberPROSPEROregistration number: CRD42016052276.

scholarly journals Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fabian Doubek ◽  
Erik Loosveld ◽  
Riender Happee ◽  
Joost de Winter
Keyword(s):  
Driving Simulator ◽  
Time Budget ◽  
Lateral Acceleration ◽  
Traffic Density ◽  
Density Level ◽  
Lane Change ◽  
Automated Driving ◽  
Human In The Loop ◽  
Short Time ◽  
Left Lane

In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non–Driving-Related Tasks in Automated Vehicles

2021 ◽  
pp. 001872082110562
Author(s):  
Xiaomeng Li ◽  
Ronald Schroeter ◽  
Andry Rakotonirainy ◽  
Jonny Kuo ◽  
Michael G. Lenné
Keyword(s):  
Mobile Phone ◽  
Real World ◽  
Simulation Experiment ◽  
Driving Simulator ◽  
Reaction Times ◽  
Driving Simulation ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Driver Performance ◽  
Vehicle Technology

Objective The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non–driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone. Background Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances. Method A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers’ visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance. Results The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers’ take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request. Conclusion HUDs can improve driver performance and lower workload when used as an NDRT interface. Application The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

scholarly journals Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving

2021 ◽  
Vol 11 (17) ◽  
pp. 7959
Author(s):  
Gregor Strle ◽  
Yilun Xing ◽  
Erika E. Miller ◽  
Linda Ng Boyle ◽  
Jaka Sodnik
Keyword(s):  
Cultural Differences ◽  
Cultural Study ◽  
Driving Simulator ◽  
Cross Cultural ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Safe Driving ◽  
Cross Cultural Study ◽  
Over Time ◽  
Highly Automated Driving

The article presents a cross-cultural study of take-over performance in highly automated driving. As take-over performance is an important measure of safe driving, potential cultural differences could have important implications for the future development of automated vehicles. The study was conducted in two culturally different locations, Seattle, WA (n = 20) and Ljubljana, Slovenia (n = 18), using a driving simulator. While driving, participants voluntarily engaged in secondary tasks. The take-over request (TOR) was triggered at a specific time during the drive, and take-over time and type of response (none, brake, steer) were measured for each participant. Results show significant differences in take-over performance between the two locations. In Seattle 30% of participants in Seattle did not respond to TOR; the remaining 70% responded by braking only, compared to Slovenian participants who all responded by either braking or steering. Participants from Seattle responded significantly more slowly to TOR (M = +1285 ms) than Slovenian participants. Secondary task engagement at TOR also had an effect, with distracted US participants’ response taking significantly longer (M = +1596 ms) than Slovenian participants. Reported differences in take-over performance may indicate cultural differences in driving behavior and trust in automated driving.

scholarly journals PLANNING AND COORDINATION IN DRIVING SIMULATION

2015 ◽  
Vol 2 (2) ◽  
pp. 40-44 ◽  
Author(s):  
Martin Schaefer
Keyword(s):  
Driving Simulator ◽  
Driving Simulation ◽  
Coordination Mechanisms ◽  
Simulation Platform ◽  
Automated Driving ◽  
Traffic Generation ◽  
Coordination Methods

We present an overview of coordination and planning tasks that we face with during the development of the AgentDrive simulation platform. We particularly describe an integration of the AgentDrive with a driving simulator OpenDS. We demonstrate how the planning and coordination mechanisms can be applied in a driving simulator for automated driving applications or realistic traffic generation. We emphasize particular planning and/or coordination methods that were already developed using AgentDrive platform.

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