Moving Into the Loop: An Investigation of Drivers’ Steering Behavior in Highly Automated Vehicles

2019 ◽  
Vol 62 (4) ◽  
pp. 671-683 ◽  
Author(s):  
Areen Alsaid ◽  
John D. Lee ◽  
Morgan Price
Keyword(s):  
Adaptive Algorithm ◽  
Manual Control ◽  
Control Algorithms ◽  
Automated Vehicles ◽  
Automated Control ◽  
The Road ◽  
Out Of The Loop ◽  
Combined Control ◽  
Driving Task ◽  
Vehicle Automation

Objective This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering—drivers steering when the vehicle is under automated control. Background Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers’ steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles. Method A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners’ manuals. Results The adaptive algorithm increased vicarious steering ( p < .001), but instructed responsibility did not ( p = .67), and there was no interaction between the algorithm and the responsibility ( p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control. Conclusion Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.

scholarly journals Augmented Reality Windshield Displays and Their Potential to Enhance User Experience in Automated Driving

i-com ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 127-149 ◽  
Author(s):  
Andreas Riegler ◽  
Philipp Wintersberger ◽  
Andreas Riener ◽  
Clemens Holzmann
Keyword(s):  
User Experience ◽  
User Study ◽  
User Preferences ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Level Of Automation ◽  
Levels Of Automation ◽  
Driving Task ◽  
Vehicle Automation ◽  
Fully Automated Driving

Abstract Increasing vehicle automation presents challenges as drivers of highly automated vehicles become more disengaged from the primary driving task. However, even with fully automated driving, there will still be activities that require interfaces for vehicle-passenger interactions. Windshield displays are a technology with a promising potential for automated driving, as they are able to provide large content areas supporting drivers in non-driving related activities. However, it is still unknown how potential drivers or passengers would use these displays. This work addresses user preferences for windshield displays in automated driving. Participants of a user study (N=63) were presented two levels of automation (conditional and full), and could freely choose preferred positions, content types, as well as size, transparency levels and importance levels of content windows using a simulated “ideal” windshield display. We visualized the results in form of heatmap data which show that user preferences differ with respect to the level of automation, age, gender, or environment aspects. These insights can help designers of interiors and in-vehicle applications to provide a rich user experience in highly automated vehicles.

The Relationship Between Takeover Request Lead Time and Drivers’ Situation Awareness for Freeway Exiting in Conditionally Automated Driving

2021 ◽  
Vol 65 (1) ◽  
pp. 1385-1389
Author(s):  
Xiaomei Tan ◽  
Yiqi Zhang
Keyword(s):  
Situation Awareness ◽  
Lead Time ◽  
Manual Control ◽  
Lead Times ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Web Based ◽  
Positive Effects ◽  
Out Of The Loop ◽  
The Relationship

Conditionally automated vehicles require the out-of-the-loop driver to intervene when the system is unable to handle forthcoming situations, such as freeway exiting. The takeover request (ToR) for exiting a freeway can be scheduled in advance. Upon a ToR, the driver needs to gain situation awareness (SA) and resume manual control. This study examined how the ToR lead time affects driver SA for resuming control and when to send the ToR is most appropriate for freeway exiting. A web-based, supervised experiment was conducted with 31 participants. Each participant experienced 12 levels of ToR lead time (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s). The results showed positive effects of longer ToR lead times (16–60 s) on driver SA for resuming control to exit from freeways in comparison to shorter ToR lead times (6–14 s), and the effects level off at 16–30 s.

Fatigue and Automation-Induced Impairments in Simulated Driving Performance

1998 ◽  
Vol 1628 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Paula A. Desmond ◽  
Peter A. Hancock ◽  
Janelle L. Monette
Keyword(s):  
Intelligent Transportation Systems ◽  
Driving Simulator ◽  
Transportation Systems ◽  
Task Demands ◽  
Manual Control ◽  
Automated Driving ◽  
The Road ◽  
Full Control ◽  
Performance Recovery ◽  
Driving Task

A driving simulator study investigated the effect of automation of the driving task on performance under fatiguing driving conditions. In the study, drivers performed both a manual drive, in which they had full control over the driving task, and an automated drive, in which the vehicle was controlled by an automated driving system. During both drives, three perturbing events occurred at early, intermediate, and late phases in the drives: in the automated drive, a failure in automation caused the vehicle to drift toward the edge of the road; in the manual drive, wind gusts resulted in the vehicle drifting in the same direction and magnitude as the “drifts” in the automated drive. Following automation failure, drivers were forced to control the vehicle manually until the system became operational again. Drivers’ lateral control of the vehicle was assessed during three phases of manual control in both drives. The results indicate that performance recovery was better when drivers had full manual control of the vehicle throughout the drive, rather than when drivers were forced to drive manually following automation failure. Drivers also experienced increased tiredness, and physical and perceptual fatigue symptoms following both drives. The findings have important implications for the design of intelligent transportation systems. Systems that reduce the driver’s perceptions of task demands of driving are likely to undermobilize effort in fatigued drivers. Thus, the results strongly support the contention that human-centered transportation strategies, in which the driver is involved in the driving task, are superior to total automation.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

scholarly journals The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe

2021 ◽  
Vol 6 (3) ◽  
pp. 43
Author(s):  
Konstantinos Gkoumas ◽  
Kyriaki Gkoktsi ◽  
Flavio Bono ◽  
Maria Cristina Galassi ◽  
Daniel Tirelli
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transport Infrastructure ◽  
Policy Framework ◽  
Strategic Decisions ◽  
Automated Vehicles ◽  
Civil Infrastructures ◽  
Vehicle Automation ◽  
Labor Requirements

Europe’s aging transportation infrastructure requires optimized maintenance programs. However, data and monitoring systems may not be readily available to support strategic decisions or they may require costly installations in terms of time and labor requirements. In recent years, the possibility of monitoring bridges by indirectly sensing relevant parameters from traveling vehicles has emerged—an approach that would allow for the elimination of the costly installation of sensors and monitoring campaigns. The advantages of cooperative, connected, and automated mobility (CCAM), which is expected to become a reality in Europe towards the end of this decade, should therefore be considered for the future development of iSHM strategies. A critical review of methods and strategies for CCAM, including Intelligent Transportation Systems, is a prerequisite for moving towards the goal of identifying the synergies between CCAM and civil infrastructures, in line with future developments in vehicle automation. This study presents the policy framework of CCAM in Europe and discusses the policy enablers and bottlenecks of using CCAM in the drive-by monitoring of transport infrastructure. It also highlights the current direction of research within the iSHM paradigm towards the identification of technologies and methods that could benefit from the use of connected and automated vehicles (CAVs).

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 Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment

2021 ◽  
Vol 13 (15) ◽  
pp. 8396
Author(s):  
Marc Wilbrink ◽  
Merle Lau ◽  
Johannes Illgner ◽  
Anna Schieben ◽  
Michael Oehl
Keyword(s):  
Urban Traffic ◽  
Automated Vehicles ◽  
Negative Effects ◽  
Communication Tools ◽  
Automated Vehicle ◽  
Additional Information ◽  
Road Users ◽  
Vulnerable Road Users ◽  
And Behaviors ◽  
Vehicle Automation

The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.

Automated control of oxygen titration in preterm infants on non-invasive respiratory support

2021 ◽  
pp. fetalneonatal-2020-321538
Author(s):  
Peter A Dargaville ◽  
Andrew P Marshall ◽  
Oliver J Ladlow ◽  
Charlotte Bannink ◽  
Rohan Jayakar ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Neonatal Intensive Care ◽  
Supplemental Oxygen ◽  
Respiratory Support ◽  
Target Range ◽  
Manual Control ◽  
Oxygen Control ◽  
Automated Control ◽  
Non Invasive ◽  
Automated Oxygen Control

ObjectiveTo evaluate the performance of a rapidly responsive adaptive algorithm (VDL1.1) for automated oxygen control in preterm infants with respiratory insufficiency.DesignInterventional cross-over study of a 24-hour period of automated oxygen control compared with aggregated data from two flanking periods of manual control (12 hours each).SettingNeonatal intensive care unit.ParticipantsPreterm infants receiving non-invasive respiratory support and supplemental oxygen; median birth gestation 27 weeks (IQR 26–28) and postnatal age 17 (12–23) days.InterventionAutomated oxygen titration with the VDL1.1 algorithm, with the incoming SpO2 signal derived from a standard oximetry probe, and the computed inspired oxygen concentration (FiO2) adjustments actuated by a motorised blender. The desired SpO2 range was 90%–94%, with bedside clinicians able to make corrective manual FiO2 adjustments at all times.Main outcome measuresTarget range (TR) time (SpO2 90%–94% or 90%–100% if in air), periods of SpO2 deviation, number of manual FiO2 adjustments and oxygen requirement were compared between automated and manual control periods.ResultsIn 60 cross-over studies in 35 infants, automated oxygen titration resulted in greater TR time (manual 58 (51–64)% vs automated 81 (72–85)%, p<0.001), less time at both extremes of oxygenation and considerably fewer prolonged hypoxaemic and hyperoxaemic episodes. The algorithm functioned effectively in every infant. Manual FiO2 adjustments were infrequent during automated control (0.11 adjustments/hour), and oxygen requirements were similar (manual 28 (25–32)% and automated 26 (24–32)%, p=0.13).ConclusionThe VDL1.1 algorithm was safe and effective in SpO2 targeting in preterm infants on non-invasive respiratory support.Trial registration numberACTRN12616000300471.

Randomised cross-over study of automated oxygen control for preterm infants receiving nasal high flow

2018 ◽  
pp. fetalneonatal-2018-315342 ◽  
Author(s):  
Peter R Reynolds ◽  
Thomas L Miller ◽  
Leonithas I Volakis ◽  
Nicky Holland ◽  
George C Dungan ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Neonatal Intensive Care ◽  
Primary Outcome Measure ◽  
High Flow ◽  
Target Range ◽  
Manual Control ◽  
Oxygen Control ◽  
Automated Control ◽  
Registration Number ◽  
Preterm Babies

ObjectiveTo evaluate a prototype automated controller (IntellO2) of the inspired fraction of oxygen (FiO2) in maintaining a target range of oxygen saturation (SpO2) in preterm babies receiving nasal high flow (HF) via the Vapotherm Precision Flow.DesignProspective two-centre order-randomised cross-over study.SettingNeonatal intensive care units.PatientsPreterm infants receiving HF with FiO2 ≥25%.InterventionAutomated versus manual control of FiO2 to maintain a target SpO2 range of 90%–95% (or 90%–100% if FiO2=21%).Main outcome measuresThe primary outcome measure was per cent of time spent within target SpO2 range. Secondary outcomes included the overall proportion and durations of SpO2 within specified hyperoxic and hypoxic ranges and the number of in-range episodes per hour.ResultsData were analysed from 30 preterm infants with median (IQR) gestation at birth of 26 (24–27) weeks, study age of 29 (18–53) days and study weight 1080 (959–1443) g. The target SpO2 range was achieved 80% of the time on automated (IntellO2) control (IQR 70%–87%) compared with 49% under manual control (IQR 40%–57%; p<0.0001). There were fewer episodes of SpO2 below 80% lasting at least 60 s under automated control (0 (IQR 0–1.25)) compared with manual control (5 (IQR 2.75–14)). There were no differences in the number of episodes per hour of SpO2 above 98% (4.5 (IQR 1.8–8.5) vs 5.5 (IQR 1.9–14); p=0.572) between the study arms.ConclusionsThe IntellO2 automated oxygen controller maintained patients in the target SpO2 range significantly better than manual adjustments in preterm babies receiving HF.Trial registration numberNCT02074774.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

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