scholarly journals Effects of Upper Extremity Immobilization and Use of a Spinner Knob on Vehicle Steering

Hand ◽  
2016 ◽  
Vol 12 (6) ◽  
pp. 597-605 ◽  
Author(s):  
Lyle T. Jackson ◽  
Matthew C. Crisler ◽  
Stephanie L. Tanner ◽  
Johnell O. Brooks ◽  
Kyle J. Jeray
Keyword(s):  
Reaction Time ◽  
Crossover Trial ◽  
Driving Simulator ◽  
Automatic Transmission ◽  
Driving Ability ◽  
Difficulty Level ◽  
Steering Wheel ◽  
Improve Accuracy ◽  
Negative Effect ◽  
Impact Driving

Background: A person’s ability to safely drive while immobilized is not well defined. Steering ability with a spinner knob during immobilization is unknown. The goal of this study is to further clarify the effect of immobilization on steering reaction time and accuracy with and without a steering wheel spinner knob. Methods: Twenty participants were enrolled in this crossover trial using a driving simulator with an automatic transmission. Five conditions were tested in a counterbalanced order. Steering reaction time and accuracy (number of errors on a dynamic steering task at 2 difficulty levels) were measured. Participants were allowed to steer with the immobilized extremity. Results: No significant differences in reaction time were observed between any conditions. Both immobilized conditions and difficulty level of the steering task led to diminished accuracy compared with controls, resulting in significantly more errors. The use of a spinner knob significantly improved the accuracy for the condition with the sugar-tong splint during the easier steering task, but this improvement was not observed in the harder steering task. There were no differences between conditions based on gender or observed use of the immobilized arm. Conclusions: Immobilization had a negative effect on steering accuracy for both the wrist splint and the sugar-tong splint condition, which may negatively impact driving ability of immobilized patients. Immobilization, regardless of spinner knob use, did not significantly impact steering reaction time. The steering wheel spinner knob did not consistently improve accuracy, and further study is needed to determine its utility.

Evaluation of Drivers’ Attention Level by a Driving Simulator

2012 ◽  
Author(s):  
Riccardo Bartolozzi ◽  
Francesco Frendo
Keyword(s):  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Automatic Transmission ◽  
Point Of View ◽  
Steering Wheel ◽  
Data Logging ◽  
Feedback Systems ◽  
Specific Test ◽  
Xpc Target ◽  
Attention Level

Diagnosis systems for evaluating driver’s attention level have become very important in the last years and have gained an increasing attention from automotive manufacturers; indeed, traffic crashes represent worldwide one of the main public health problems and many accidents are demonstrated to be due to drivers’ hypovilance. In this work a driving simulator and specific test tools were developed to validate the possibility of monitoring the drivers’ attention level and capability. The driving simulator is equipped with a fixed cockpit and a single front screen. The cockpit reproduces the man-machine interface of a typical medium class car with automatic transmission, i.e. the driver interacts with the simulator by means of the throttle and brake pedals and the steering wheel. The pedals are endowed with passive feedback systems, whereas an electric motor applies an active feedback torque on the steering wheel. From the software point of view, the simulator is managed by four PCs connected by a LAN. Two of them are dedicated to the simulation of vehicle dynamics and for data logging, while the other two run the graphical scenario software. This includes a motorway area connected to an urban area with an autonomous traffic. The vehicle model was built with a block architecture in the Matlab/Simulink environment and is run in real-time by using the xPC Target toolbox. A specific block, consisting of an in-house developed 14 d.o.f. model, was set up to represents vehicle dynamics. The driving simulator is currently employed in experimental campaigns acquiring about 60 signals of driver’s input and vehicle quantities. In order to evaluate the drivers’ attention level, two specific tests were developed, which aim at measuring the drivers’ reaction time and ability in sudden events such as a brake manoeuvre or a sudden lateral skid. In the paper the driving simulator hardware and software are presented. The tests procedures, which were developed to investigate the driver’s attention, are also discussed with reference to some tests.

341 Acute effects of seltorexant, a selective orexin-2 antagonist (JNJ- 42847922), on driving after bedtime administration

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A136-A136
Author(s):  
S Brooks ◽  
R G J A Zuiker ◽  
G E Jacobs ◽  
I Kezic ◽  
A Savitz ◽  
...  
Keyword(s):  
Postural Stability ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Subjective Effects ◽  
Driving Ability ◽  
Post Dose ◽  
Double Blind ◽  
Increased Risk ◽  
Subjective Sleepiness ◽  
The Difference

Abstract Introduction Seltorexant (JNJ-42847922), a potent and selective antagonist of the human orexin-2 receptor, is being developed for the treatment of major depressive disorder. Seltorexant also has sleep-promoting properties. Investigating the effects of sleep-promoting medications on driving is important because some of these agents (e.g. GABAA receptor agonists) may be associated with increased risk of motor vehicle accidents. We evaluated the effect of seltorexant on driving after forced awakening at night, using a validated driving simulator. Methods This double-blind, placebo and active-controlled, randomized, 3-way cross-over study was conducted in 18 male and 18 female healthy subjects. All subjects received seltorexant 40 mg, zolpidem 10 mg, or placebo 15 minutes before bedtime. Eighteen subjects were awakened at 2- and 6-hours post-dose, and the other 18 at 4- and 8-hours post-dose. At those timepoints, pharmacokinetics, objective (standard deviation of the lateral position [SDLP]) and subjective effects (using Perceived Driving Quality and Effort Scales) on driving ability, postural stability and subjective sleepiness were assessed. Results For seltorexant, the SDLP difference from placebo (95% confidence interval) at 2-, 4-, 6- and 8-hours post-dose was 3.9 cm (1.26, 6.60), 0.9 cm (-1.08, 2.92), 1.1 cm (-0.42, 2.63), and 0.6 cm (-2.75, 1.55), respectively vs. 9.6 cm (6.97, 12.38), 6.6 cm (3.53, 9.60), 4.7 cm (1.46, 7.85), and 1.3cm (-1.16, 3.80), respectively for zolpidem. The difference from placebo was significant at 2-hours after taking seltorexant, while the difference from placebo was significant at 2, 4 and 6-hours after zolpidem. Subjective driving quality was decreased for both drugs at all time points and driving effort was increased up to 4-hours post-dose for both medications. Subjective sleepiness showed a significant increase compared to placebo 2- and 4-hours after administration of either drug. Postural stability was decreased up to 2-hours after administration of seltorexant, and up to 4-hours after administration of zolpidem. Conclusion Compared to zolpidem, objective effects on driving performance were more transient after seltorexant administration and largely normalized by 4–6 hours post-dose. Support (if any) This work was sponsored by Janssen R&D.

Study on Driver and Vehicle System With Lane-Tracking Control System Using Steering Torque Input

2001 ◽  
Author(s):  
Masao Nagai ◽  
Hidehisa Yoshida ◽  
Kiyotaka Shitamitsu ◽  
Hiroshi Mouri
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Driving Simulator ◽  
Tracking System ◽  
Torque Control ◽  
Steering Wheel ◽  
Control Input ◽  
Steering Angle ◽  
Lane Tracking ◽  
Steering Torque

Abstract Although the vast majority of lane-tracking control methods rely on the steering wheel angle as the control input, a few studies have treated methods using the steering torque as the input. When operating vehicles especially at high speed, drivers typically do not grip the steering wheel tightly to prevent the angle of the steering wheel from veering off course. This study proposes a new steering assist system for a driver not with the steering angle but the steering torque as the input and clarifies the characteristics and relative advantages of the two approaches. Then using a newly developed driving simulator, characteristics of human drivers and the lane-tracking system based on the steering torque control are investigated.

scholarly journals The Effects of Vehicle Level Of Automation and Warning Type on Responses to Vehicle Hacking

2019 ◽  
Vol 63 (1) ◽  
pp. 1977-1977
Author(s):  
Wyatt McManus ◽  
Jing Chen
Keyword(s):  
Driving Simulator ◽  
Blind Spot ◽  
Steering Wheel ◽  
Automated Driving ◽  
Impact Surface ◽  
Level Of Automation ◽  
Surface Transportation ◽  
Levels Of Automation ◽  
Automation Level ◽  
Different Levels

Modern surface transportation vehicles often include different levels of automation. Higher automation levels have the potential to impact surface transportation in unforeseen ways. For example, connected vehicles with higher levels of automation are at a higher risk for hacking attempts, because automated driving assistance systems often rely on onboard sensors and internet connectivity (Amoozadeh et al., 2015). As the automation level of vehicle control rises, it is necessary to examine the effect different levels of automation have on the driver-vehicle interactions. While research into the effect of automation level on driver-vehicle interactions is growing, research into how automation level affects driver’s responses to vehicle hacking attempts is very limited. In addition, auditory warnings have been shown to effectively attract a driver’s attention while performing a driving task, which is often visually demanding (Baldwin, 2011; Petermeijer, Doubek, & de Winter, 2017). An auditory warning can be either speech-based containing sematic information (e.g., “car in blind spot”) or non-sematic (e.g., a tone, or an earcon), which can influence driver behaviors differently (Sabic, Mishler, Chen, & Hu, 2017). The purpose of the current study was to examine the effect of level of automation and warning type on driver responses to novel critical events, using vehicle hacking attempts as a concrete example, in a driving simulator. The current study compared how level of automation (manual vs. automated) and warning type (non-semantic vs. semantic) affected drivers’ responses to a vehicle hacking attempt using time to collision (TTC) values, maximum steering wheel angle, number of successful responses, and other measures of response. A full factorial between-subjects design with the two factors made four conditions (Manual Semantic, Manual Non-Semantic, Automated Semantic, and Automated Non-Semantic). Seventy-two participants recruited using SONA ( odupsychology.sona-systems.com ) completed two simulated drives to school in a driving simulator. The first drive ended with the participant safely arriving at school. A two-second warning was presented to the participants three quarters of the way through the second drive and was immediately followed by a simulated vehicle hacking attempt. The warning either stated “Danger, hacking attempt incoming” in the semantic conditions or was a 500 Hz sine tone in the non-semantic conditions. The hacking attempt lasted five seconds before simulating a crash into a vehicle and ending the simulation if no intervention by the driver occurred. Our results revealed no significant effect of level of automation or warning type on TTC or successful response rate. However, there was a significant effect of level of automation on maximum steering wheel angle. This is a measure of response quality (Shen & Neyens, 2017), such that manual drivers had safer responses to the hacking attempt with smaller maximum steering wheel angles. In addition, an effect of warning type that approached significance was also found for maximum steering wheel angle such that participants who received a semantic warning had more severe and dangerous responses to the hacking attempt. The TTC and successful response results from the current experiment do not match those in the previous literature. The null results were potentially due to the warning implementation time and the complexity of the vehicle hacking attempt. In contrast, the maximum steering wheel angle results indicated that level of automation and warning type affected the safety and severity of the participants’ responses to the vehicle hacking attempt. This suggests that both factors may influence responses to hacking attempts in some capacity. Further research will be required to determine if level of automation and warning type affect participants ability to safely respond to vehicle hacking attempts. Acknowledgments. We are grateful to Scott Mishler for his assistance with STISIM programming and Faye Wakefield, Hannah Smith, and Pettie Perkins for their assistance in data collection.

scholarly journals In Masks we Trust: Explicit and Implicit Reactions to Masked Faces Vary by Voting Intention

2021 ◽  
Author(s):  
Gordon Ingram ◽  
Erick Gustavo Chuquichambi ◽  
William Jimenez-Leal ◽  
Antonio Olivera-LaRosa
Keyword(s):  
Reaction Time ◽  
Social Desirability ◽  
Political Orientation ◽  
Public Places ◽  
Reaction Time Measure ◽  
Positive Effects ◽  
Spanish Speaking ◽  
Negative Effect ◽  
English Speaking ◽  
The Uk

The COVID-19 pandemic has caused controversy over new norms of mask-wearing in public places. An online experiment previously showed that people from several Spanish-speaking countries perceived faces wearing medical-style masks as more trustworthy, socially desirable, and likely to be ill, compared to control faces without a mask. We replicated and extended these methods with 1241 English-speaking participants from the UK and USA, adding questions on political orientation and voting intention, and including the online-VAAST task to test the effects of masks on an implicit reaction-time measure. The positive effects of masks on trustworthiness and social desirability were replicated, but the negative effect of masks on perceptions of healthiness was reversed. Participants were also quicker to approach masked faces. Conservative voters’ explicit and implicit reactions to masked faces were less favorable than those of liberals, demonstrating that masks are viewed positively by many but continue to be politically controversial.

[P2-285]: SELF-EVALUATION OF DRIVING ABILITY THROUGH A DRIVING SIMULATOR EXPERIMENT: DIFFERENCES BETWEEN PATIENTS WITH MILD COGNITIVE IMPAIRMENT (MCI) AND HEALTHY ELDERLY DRIVERS

2017 ◽  
Vol 13 (7S_Part_15) ◽  
pp. P724-P724 ◽  
Author(s):  
Stella Fragkiadaki ◽  
Dionysia Kontaxopoulou ◽  
Ion N. Beratis ◽  
Dimosthenis Pavlou ◽  
Nikolaos Andronas ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Driving Simulator ◽  
Driving Ability ◽  
Self Evaluation ◽  
Elderly Drivers ◽  
Healthy Elderly

An identification algorithm of driver steering characteristics based on backpropagation neural network

2019 ◽  
Vol 233 (9) ◽  
pp. 2333-2342
Author(s):  
Shangyu Zhao ◽  
Guoying Chen ◽  
Min Hua ◽  
Changfu Zong
Keyword(s):  
Neural Network ◽  
Driving Simulator ◽  
Careful Analysis ◽  
Angular Speed ◽  
Identification Accuracy ◽  
Steering Wheel ◽  
Identification Algorithm ◽  
Backpropagation Neural Network ◽  
Feature Parameter ◽  
Steering Characteristics

This paper presents a novel identification method of driver steering characteristics based on backpropagation neural network. First, a driving simulator is built to collect required driving data. After careful analysis, three feature parameters that reflect driver steering characteristics are determined, including the average steering wheel angular speed, the standard deviation of the steering wheel angle, and the average vehicle longitudinal speed. Then, steering feature parameter vectors are extracted from raw data and clustered by the K-means algorithm. According to the clustering result, driver steering characteristics are divided into three types: cautious, average, and aggressive. Subsequently, a backpropagation neural network with two hidden layers is designed and trained to identify the types of feature parameter vectors. Verification results show that the established backpropagation neural network has high identification accuracy and good generalization ability for the identification of driver steering characteristics.

scholarly journals Driving Behaviour in Depression: Findings from a Driving Simulator Study

Safety ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 70
Author(s):  
Vagioula Tsoutsi ◽  
Dimitris Dikeos ◽  
Maria Basta ◽  
Maria Papadakaki
Keyword(s):  
Sleep Disturbance ◽  
Sleep Disturbances ◽  
Driving Simulator ◽  
Executive Dysfunction ◽  
Lateral Position ◽  
Driving Ability ◽  
Aggressive Driving ◽  
The Road ◽  
Driving Behaviour ◽  
Preceding Vehicle

Depression is characterized by mental, emotional and executive dysfunction. Among its symptoms, sleep disturbance and anxiety are very common. The effects of depression and its treatment may have an impact on driving behaviour. In order to evaluate driving performance in depression, 13 patients and 18 healthy controls completed questionnaires and scales and were tested in a driving simulator. Driving simulator data included lateral position (LP), speed and distance from the preceding vehicle. History of collisions was associated with depression, body mass index (BMI) and next-day consequences of sleep disturbance. Aggressive driving was associated with fatigue and sleep disturbances. Concerning driving simulator data, a reduced ability to maintain constant vehicle velocity was positively correlated to BMI and insomnia. An LP towards the middle of the road was associated with anxiety. On the other hand, an LP towards the shoulder was associated with depression and next-day consequences of sleep disturbance, while a positive correlation was found between distance from the preceding vehicle and use of drugs with potential hypnotic effects; both these findings show that patients suffering from depression seem to realize the effects of certain symptoms on their driving ability and thus drive in a more defensive way than controls.

Impact of Cognitive Distractions on Drivers’ Hazard Anticipation Behavior in Complex Scenarios

2019 ◽  
Vol 2673 (9) ◽  
pp. 440-451 ◽  
Author(s):  
Yalda Ebadi ◽  
Donald L. Fisher ◽  
Shannon C. Roberts
Keyword(s):  
Response Time ◽  
Cell Phone ◽  
Driving Simulator ◽  
Mock Cell ◽  
Cell Phone Use ◽  
Road Users ◽  
Driving Skill ◽  
Cognitive Distraction ◽  
Negative Effect ◽  
Vulnerable Road Users

Distraction is one of the main causes of traffic crashes. The effect of cell phones, a major contributor to cognitive distraction, on response time has tested in many simulator studies. Response time is not the only driving skill affected by cell phone conversations, however. Specifically, it is known that cell phone usage contributes to a reduction in hazard anticipation skills at intersections, and there are many other scenarios where cell phone conversations could have a detrimental effect. The objective of this study is to determine whether cognitive distractions associated with cell phone use affect hazard anticipation, not only at intersections but also in other scenarios which contain latent hazards. Twenty-four drivers navigated 18 scenarios on a driving simulator twice, with their eye movements continuously monitored. During one drive, participants performed a hands-free mock cell phone task while driving; on the other drive, they navigated through the virtual world without any additional tasks apart from the primary task of driving. The scenarios were classified into two types: intersection and non-intersection. Using a logistic regression model within the framework of generalized estimation equations, it was determined that the proportion of anticipatory glances toward potential hazards was reduced significantly for all scenarios when drivers were engaged in a mock cell phone task. Perhaps the most disturbing finding is that this was true both at work zones and at marked midblock crosswalks, scenarios which often endanger vulnerable road users. In summary, the study found a negative effect of cognitive distraction on latent hazard anticipation.

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