Analysis of Disengagements in Semi-Autonomous Vehicles: Drivers’ Takeover Performance and Operational Implications

This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the simulation experiment. Independent variables considered for the study were the age of the driver, the speed at the time of disengagement, and the time at which the disengagement occurred (i.e., how long automation was engaged for). The study shows that changes in the vehicle speed significantly affect all the variables investigated, pointing to the importance of setting up thresholds for maximum operational speed of vehicles driven in autonomous mode when the human driver serves as back-up. The results shows that the establishment of an operational threshold could reduce the maximum drift and lead to better control during takeover, perhaps warranting a lower speed limit than conventional vehicles. With regards to the age variable, neither the response times analysis nor the drift analysis provide support for any claim to limit the age of drivers of semi-autonomous vehicles

Predicting the efficacy of simulator-based training using a perceptual judgment task versus questionnaire-based measures of presence

The quality of a virtual environment, as characterized by factors such as presence and fidelity, is of interest to developers and users of simulators for many reasons, not least because both factors have been linked to improved outcomes in training as well as a reduced incidence of simulator sickness. Until recently, most approaches to measuring these factors have been based on subjective, postexposure questioning. This approach has, however, been criticized because of the shortcomings of self-report and the need to delay feedback or interrupt activity. To combat these problems, recent papers on the topic have proposed the use of behavioral measures to assess simulators and predict training outcomes. Following their lead, this paper makes use of a simple perceptual task in which users are asked to estimate their simulated speed within the environment. A longitudinal study of training outcomes using two of the simulators revealed systematic differences in task performance that matched differences measured using the perceptual task in a separate group of control subjects. A separate analysis of two standard presence questionnaires revealed that they were able to predict learning outcomes on a per individual basis, but that they were insensitive to the differences between the two simulators. The paper concludes by explaining how behavioral measures of the type proposed here can complement questionnaire-based studies, helping to motivate design aspects of new simulators, prompting changes to existing systems, and constraining training scenarios to maximize their efficacy

Functional requirements for the man-vehicle systems research facility

The NASA Ames Research Center proposed a man-vehicle systems research facility to support flight simulation studies which are needed for identifying and correcting the sources of human error associated with current and future air carrier operations. The organization of research facility is reviewed and functional requirements and related priorities for the facility are recommended based on a review of potentially critical operational scenarios. Requirements are included for the experimenter's simulation control and data acquisition functions, as well as for the visual field, motion, sound, computation, crew station, and intercommunications subsystems. The related issues of functional fidelity and level of simulation are addressed, and specific criteria for quantitative assessment of various aspects of fidelity are offered. Recommendations for facility integration, checkout, and staffing are included

Connected and Automated Vehicle Based Intersection Maneuver Assist Systems (CAVIMAS) and Their Impact on Driver Behavior, Acceptance, and Safety

Intersection crashes can be potentially mitigated by leveraging deployments of vehicle-to-infrastructure (V2I) and vehicle-to- vehicle (V2V) safety management solutions. However, it is equally critical that these deployments are undertaken in tandem with interventions based on human factors evidence relating to the content and presentation of such solutions. This driving simulator study designed and evaluated a conceptual system - Connected and Automated Vehicle based Intersection Maneuver Assist Systems (CAVIMAS) - aimed at assisting drivers with intersection maneuvers by leveraging connected infrastructure and providing real-time guidance and warnings and active vehicle controls. Results indicate that human factors considerations for the design and deployment of such systems remain paramount, given the findings related to drivers’ trust and acceptance of these systems as measured via surveys and by examining actual driving behaviors.Center for Connected and Automated Transportationhttps://deepblue.lib.umich.edu/bitstream/2027.42/156048/4/Connected_and_Automated_Vehicle_Based_Intersection_Maneuver_Assist_Systems_CAVIMAS.pd

Feasibility and validity of a low-cost racing simulator in driving assessment after stroke

There is a myriad of methodologies to assess driving performance after a stroke. These include psychometric tests, driving simulation, questionnaires, and/or road tests. Research-based driving simulators have emerged as a safe, convenient way to assess driving performance after a stroke. Such traditional research simulators are useful in recreating street traffic scenarios, but are often expensive, with limited physics models and graphics rendering. In contrast, racing simulators developed for motorsport professionals and enthusiasts offer high levels of realism, run on consumer-grade hardware, and can provide rich telemetric data. However, most offer limited simulation of traffic scenarios. This pilot study compares the feasibility of research simulation and racing simulation in a sample with minor stroke. We determine that the racing simulator is tolerated well in subjects with a minor stroke. There were correlations between research and racing simulator outcomes with psychometric tests associated with driving performance, such as the Trails Making Test Part A, Snellgrove Maze Task, and the Motricity Index. We found correlations between measures of driving speed on a complex research simulator scenario and racing simulator lap time and maximum tires off track. Finally, we present two models, using outcomes from either the research or racing simulator, predicting road test failure as linked to a previously published fitness-to-drive calculator that uses psychometric screening

Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator

Motion cueing in driving simulators for research applications

This research investigated the perception of self-motion in driving simulation, focussing on the dynamic cues produced by a motion platform. The study was undertaken in three stages, evaluating various motion cueing techniques based on both subjective ratings of realism and objective measures of driver performance. Using a Just Noticeable Difference methodology, Stage 1 determined the maximum perceptible motion scaling for platform movement in both translation and tilt. Motion cues scaled by 90% or more could not be perceptibly differentiated from unscaled motion. This result was used in Stage 2‟s examination of the most appropriate point in space at which the platform translations and rotations should be centred (Motion Reference Point, MRP). Participants undertook two tracking tasks requiring both longitudinal (braking) and lateral (steering) vehicle control. Whilst drivers appeared unable to perceive a change in MRP from head level to a point 1.1m lower, the higher position (closer to the vestibular organs) did result in marginally smoother braking, corresponding to the given requirements of the longitudinal driving task. Stage 3 explored the perceptual trade-off between the specific force error and tilt rate error generated by the platform. Three independent experimental factors were manipulated: motion scale-factor, platform tilt rate and additional platform displacement afforded by a XY-table. For the longitudinal task, slow tilt that remained sub-threshold was perceived as the most realistic, especially when supplemented by the extra surge of the XY-table. However, braking task performance was superior when a more rapid tilt was experienced. For the lateral task, perceived realism was enhanced when motion cues were scaled by 50%, particularly with added XY-sway. This preference was also supported by improvements in task accuracy. Participants ratings were unmoved by changing tilt rate, although rapid tilt did result in more precise lane control. Several interactions were also observed, most notably between platform tilt rate and XY-table availability. When the XY-table was operational, driving task performance varied little between sub-threshold and more rapid tilt. However, while the XY-table was inactive, both driving tasks were better achieved in conditions of high tilt rate. An interpretation of these results suggests that without the benefit of significant extra translational capability, priority should be given to the minimisation of specific force error through motion cues presented at a perceptibly high tilt rate. However, XY-table availability affords the simulator engineer the luxury of attaining a slower tilt that provides both accurate driving task performance and accomplishes maximum perceived realism

Applying serious games to assess driver : information system ergonomics

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

EFFECTS OF READING TEXT WHILE DRIVING ANALYSIS OF 200 HONOLULU TAXI DRIVERS ON A VS500M SIMULATOR

M.S. Thesis. University of Hawaiʻi at Mānoa 2018