Behavioural Modelling of Automated to Manual Control Transition in Conditionally Automated Driving

2022 ◽  
Author(s):  
Muhammad Sajjad Ansar ◽  
Nael Alsaleh ◽  
Bilal Farooq
Keyword(s):  
Manual Control ◽  
Automated Driving ◽  
Behavioural Modelling

scholarly journals Effect of Changes in Levels of Automated Driving on Manual Control Recovery

2019 ◽  
Vol 52 (19) ◽  
pp. 79-84
Author(s):  
Genya Abe ◽  
Kenji Sato ◽  
Nobuyuki Uchida ◽  
Makoto Itoh
Keyword(s):  
Manual Control ◽  
Automated Driving

Takeover Request Design in Automated Driving: A Systematic Review

2021 ◽  
Vol 65 (1) ◽  
pp. 868-872
Author(s):  
Kevin Joel Salubre ◽  
Dan Nathan-Roberts
Keyword(s):  
Systematic Review ◽  
Current Literature ◽  
Autonomous Vehicles ◽  
Manual Control ◽  
Automated Driving ◽  
Lateral Control ◽  
Vehicle System ◽  
Level 3

Autonomous vehicles (AV) with “level 3” automation and above are expected to take full longitudinal and lateral control, which relinquishes the driver from manual control and allows for engagement with non-driving-related tasks. Despite the advance nature of a level 3 vehicle, system limitations can occur, and the driver is expected to re-engage in manual driving at a moment’s notice. Current literature has been focused on takeover performance during a takeover request (TOR) and the effects of multimodal warnings, but there is little consensus on how modality stimulus is presented. This systematic review summarizes the current designs and implementations of TORs of level 3 AVs and above. Identified themes in the review were categorized into three sections: non-driving-related tasks, driving scenarios, and takeover modality. A summary of how researchers utilized these themes in the current literature are discussed as well as implications and further research.

Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non–Driving Related Tasks on Takeover Time and Takeover Quality

2019 ◽  
Vol 61 (4) ◽  
pp. 596-613 ◽  
Author(s):  
Frederik Naujoks ◽  
Christian Purucker ◽  
Katharina Wiedemann ◽  
Claus Marberger
Keyword(s):  
Vehicle Control ◽  
Manual Control ◽  
State Transitions ◽  
Automated Driving ◽  
Test Drive ◽  
Wizard Of Oz ◽  
Driving Function ◽  
Take Back ◽  
Naturalistic Driving ◽  
Lane Position

Objective: This study aimed at investigating the driver’s takeover performance when switching from working on different non–driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. Background: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. Method: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. Results: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver’s lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly “harmless.” Conclusion: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. Application: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.

scholarly journals Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

2019 ◽  
Vol 61 (7) ◽  
pp. 1037-1065 ◽  
Author(s):  
Callum D. Mole ◽  
Otto Lappi ◽  
Oscar Giles ◽  
Gustav Markkula ◽  
Franck Mars ◽  
...  
Keyword(s):  
Motor Coordination ◽  
Reaction Times ◽  
Manual Control ◽  
Future Research ◽  
Steering Control ◽  
Automated Driving ◽  
Motor Behaviors ◽  
Human Driver ◽  
Active Feedback ◽  
The Impact

Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

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.

scholarly journals Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control

2017 ◽  
Vol 59 (4) ◽  
pp. 689-705 ◽  
Author(s):  
Alexander Eriksson ◽  
Neville A. Stanton
Keyword(s):  
Time Pressure ◽  
Manual Control ◽  
Inclusive Design ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Policy Makers ◽  
Short Time Span ◽  
Vehicle Systems ◽  
Transition Times ◽  
Short Time

Objective: The aim of this study was to review existing research into driver control transitions and to determine the time it takes drivers to resume control from a highly automated vehicle in noncritical scenarios. Background: Contemporary research has moved from an inclusive design approach to adhering only to mean/median values when designing control transitions in automated driving. Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control. We found a paucity in research into more frequent scenarios for control transitions, such as planned exits from highway systems. Method: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems. Results: Significantly longer control transition times were found between driving with and without secondary tasks. Control transition times were substantially longer than those reported in the peer-reviewed literature. Conclusion: We found that drivers take longer to resume control when under no time pressure compared with that reported in the literature. Moreover, we found that drivers occupied by a secondary task exhibit larger variance and slower responses to requests to resume control. Workload scores implied optimal workload. Application: Intra- and interindividual differences need to be accommodated by vehicle manufacturers and policy makers alike to ensure inclusive design of contemporary systems and safety during control transitions.

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.

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