scholarly journals Deceleration Assistance Mitigated the Trade-off Between Sense of Agency and Driving Performance

2021 ◽  
Vol 12 ◽  
Author(s):  
Wen Wen ◽  
Sonmin Yun ◽  
Atsushi Yamashita ◽  
Brandon D. Northcutt ◽  
Hajime Asama
Keyword(s):  
Human Error ◽  
Driving Simulator ◽  
Driving Performance ◽  
Sense Of Agency ◽  
Joint Control ◽  
Experimental Task ◽  
Collision Risk ◽  
Driving Assistance ◽  
The Face ◽  
Human Sense

Driving assistance technology has gained traction in recent years and is becoming more widely used in vehicles. However, drivers usually experience a reduced sense of agency when driving assistance is active even though automated assistance improves driving performance by reducing human error and ensuring quick reactions. The present study examined whether driving assistance can maintain human sense of agency during early deceleration in the face of collision risk, compared with manual deceleration. In the experimental task, participants decelerate their vehicle in a driving simulator to avoid collision with a vehicle that suddenly cut in front of them and decelerated. In the assisted condition, the system performed deceleration 100 ms after the cut-in. Participants were instructed to decelerate their vehicle and follow the vehicle that cut-in. This design ensured that the deceleration assistance applied a similar control to the vehicle as the drivers intended to, only faster and smoother. Participants rated their sense of agency and their driving performance. The results showed that drivers maintained their sense of agency and improved driving performance under driving assistance. The findings provided insights into designing driving assistance that can maintain drivers’ sense of agency while improving future driving performance. It is important to establish a mode of joint-control in which the system shares the intention of human drivers and provides improved execution of control.

scholarly journals Eco-Driving Performance Assessment With in-Car Visual and Haptic Feedback Assistance

2011 ◽  
Vol 11 (4) ◽  
Author(s):  
S. Azzi ◽  
G. Reymond ◽  
F. Mérienne ◽  
A. Kemeny
Keyword(s):  
Haptic Feedback ◽  
Driving Simulator ◽  
Driving Performance ◽  
Additional Contribution ◽  
Feedback Information ◽  
Verbal Instructions ◽  
Significant Difference ◽  
Driving Assistance ◽  
Driving Task ◽  
Polluting Emissions

In this experiment, 28 participants completed an urban driving task in a highly immersive driving simulator at Renault’s Technical Centre for Simulation. This simulator provides a 150 deg field of view in a fully instrumented cockpit. Two different eco-driving assistance devices were added: a visual display on the midconsole and a force feedback system on the gas pedal, in order to apply an additionnal reaction torque on drivers’ foot. The feedback information was computed by comparing the car’s instant acceleration with an optimal acceleration level based on a proprietary consumption model of a Renault diesel engine. This experiment has three main goals: I. Assess the contribution of verbal instructions to eco-driving performance; II. Quantify the additional contribution generated by two eco-driving assistance systems (visual and haptic); III. Measure drivers’ acceptance of haptic eco-driving assistance system. Basic eco-driving instructions, such as changing gears under 2000 Rpm, yield significant decrease of polluting emissions. Assisting drivers with visual, haptic, or visual-haptic on-board devices, in addition to low engine speed verbal instructions, lead to supplementary significant savings of polluting emissions. There is no significant difference between assistance feedback type; suggesting that the haptic feedback provides the same ecoperformance as visual feedback. In particular, subjects show good adaptation to the haptic feedback pedal at first utilization of the system. They apparently relied more on haptic modality to achieve the eco-driving task, when they used both visual and haptic assistance.

Effects of Individual Differences, Attention, and Memory Deficits on Driver Distraction

2020 ◽  
Vol 64 (1) ◽  
pp. 1196-1201
Author(s):  
Alejandro A. Arca ◽  
Kaitlin M. Stanford ◽  
Mustapha Mouloua
Keyword(s):  
Individual Differences ◽  
Driving Simulator ◽  
Memory Deficits ◽  
Driving Performance ◽  
Driver Distraction ◽  
Phone Call ◽  
Traffic Density ◽  
Attention And Memory ◽  
Adhd Diagnosis ◽  
Simulated Driving

The current study was designed to empirically examine the effects of individual differences in attention and memory deficits on driver distraction. Forty-eight participants consisting of 37 non-ADHD and 11 ADHD drivers were tested in a medium fidelity GE-ISIM driving simulator. All participants took part in a series of simulated driving scenarios involving both high and low traffic conditions in conjunction with completing a 20-Questions task either by text- message or phone-call. Measures of UFOV, simulated driving, heart rate variability, and subjective (NASA TLX) workload performance were recorded for each of the experimental tasks. It was hypothesized that ADHD diagnosis, type of cellular distraction, and traffic density would affect driving performance as measured by driving performance, workload assessment, and physiological measures. Preliminary results indicated that ADHD diagnosis, type of cellular distraction, and traffic density affected the performance of the secondary task. These results provide further evidence for the deleterious effects of cellphone use on driver distraction, especially for drivers who are diagnosed with attention-deficit and memory capacity deficits. Theoretical and practical implications are discussed, and directions for future research are also presented.

scholarly journals Driving Performance and Its Correlation with Neuropsychological Tests in Senior Drivers with Cognitive Impairment in Japan

2021 ◽  
Vol 79 (4) ◽  
pp. 1575-1587
Author(s):  
Zhouyuan Peng ◽  
Hiroyuki Nishimoto ◽  
Ayae Kinoshita
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Functions ◽  
Neuropsychological Tests ◽  
Driving Simulator ◽  
Driving Performance ◽  
Impaired Driving ◽  
Dementia Patients ◽  
Neuropsychological Factors ◽  
Senior Drivers

Background: With the rapid aging of the population, the issue of driving by dementia patients has been causing increasing concern worldwide. Objective: To investigate the driving difficulties faced by senior drivers with cognitive impairment and identify the specific neuropsychological tests that can reflect specific domains of driving maneuvers. Methods: Senior drivers with cognitive impairment were investigated. Neuropsychological tests and a questionnaire on demographic and driving characteristics were administered. Driving simulator tests were used to quantify participants’ driving errors in various domains of driving. Results: Of the 47 participants, 23 current drivers, though they had better cognitive functions than 24 retired drivers, were found to have impaired driving performance in the domains of Reaction, Starting and stopping, Signaling, and Overall (wayfinding and accidents). The parameters of Reaction were significantly related to the diagnosis, and the scores of MMSE, TMT-A, and TMT-B. As regards details of the driving errors, “Sudden braking” was associated with the scores of MMSE (ρ= –0.707, p < 0.01), BDT (ρ= –0.560, p < 0.05), and ADAS (ρ= 0.758, p < 0.01), “Forgetting to use turn signals” with the TMT-B score (ρ= 0.608, p < 0.05), “Centerline crossings” with the scores of MMSE (ρ= –0.582, p < 0.05) and ADAS (ρ= 0.538, p < 0.05), and “Going the wrong way” was correlated with the score of CDT (ρ= –0.624, p < 0.01). Conclusion: Different neuropsychological factors serve as predictors of different specific driving maneuvers segmented from driving performance.

Driving Performance Deficits Despite Concussion Symptom Resolution: A Case Report

2018 ◽  
Vol 23 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Nicole L. Hoffman ◽  
Hannes Devos ◽  
Julianne D. Schmidt
Keyword(s):  
Case Report ◽  
Cognitive Deficits ◽  
Processing Speed ◽  
Driving Simulator ◽  
Driving Performance ◽  
Performance Study ◽  
Male College Student ◽  
Performance Deficits ◽  
Male College ◽  
Complex Attention

Driving performance prior to concussion is not commonly available to help clinicians identify when deficits return to a preinjury status. This case report examines driving performance prior to and following concussion in a 20-year-old male college student. He initially volunteered as a control for a separate driving performance study. He sustained a concussion 18 months later, and was asked to complete the same driving tasks as previous testing once he was asymptomatic. Poor driving simulator performance and subtle cognitive deficits in complex attention and processing speed were evident despite being symptom-free. Our findings may be useful when considering readiness to drive postconcussion.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

scholarly journals Fragrance Impact on Driving Performance

2016 ◽  
Vol 2 (5) ◽  
pp. 21
Author(s):  
Masria Mustafa ◽  
Norazni Rustam ◽  
Rosfaiizah Siran
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Driving Simulator ◽  
Human Subjects ◽  
Publishing House ◽  
Driving Performance ◽  
Speed Reduction ◽  
International Publishing ◽  
Open Access Article

Previous studies have indicated that certain types of fragrance in the vehicle are useful in keeping the driver alert. This study was conducted to evaluate the effect of lavender or vanilla flavor fragrances toward driving performance. Ten human subjects were tested using the driving simulator in three different conditions; driving with vanilla, lavender flavor fragrance and driving without fragrance. A questionnaire was distributed to examine the emotion states of the driver after driving the simulator. Our results indicate that fragrance did not affect the speed reduction. The emotions of the drivers were calm due to the presence of the fragrance.2398-4279 © 2017 The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, UniversitiTeknologi MARA, Malaysia.Keywords: driving performance, vehicle fragrance, speed reduction

Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

2021 ◽  
pp. 036119812110186
Author(s):  
Samira Ahangari ◽  
Mansoureh Jeihani ◽  
Anam Ardeshiri ◽  
Md Mahmudur Rahman ◽  
Abdollah Dehzangi
Keyword(s):  
Random Forest ◽  
Driving Simulator ◽  
Situational Awareness ◽  
True Positive Rate ◽  
The United States ◽  
Driving Performance ◽  
Lane Changing ◽  
Wide Range ◽  
Positive Rate ◽  
Better Than

Distracted driving is known to be one of the main causes of crashes in the United States, accounting for about 40% of all crashes. Drivers’ situational awareness, decision-making, and driving performance are impaired as a result of temporarily diverting their attention from the primary task of driving to other unrelated tasks. Detecting driver distraction would help in adapting the most effective countermeasures. To tackle this problem, we employed a random forest (RF) classifier, one of the best classifiers that has attained promising results for a wide range of problems. Here, we trained RF using the data collected from a driving simulator, in which 92 participants drove under six different distraction scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performance measures such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Using the RF method, we achieved 76.5% prediction accuracy on the independent test set, which is over 8.2% better than results reported in previous studies. We also obtained a 76.6% true positive rate, which is 14% better than those reported in previous studies. Such results demonstrate the preference of RF over other machine learning methods to identify driving distractions.

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