scholarly journals Driver behavior and the use of automation in real-world driving

2021 ◽  
Vol 158 ◽  
pp. 106217
Author(s):  
Pnina Gershon ◽  
Sean Seaman ◽  
Bruce Mehler ◽  
Bryan Reimer ◽  
Joseph Coughlin
Keyword(s):  
Real World ◽  
Driver Behavior

scholarly journals Driving Simulators for Human Vehicle Interaction Design

2015 ◽  
Vol 1 (1) ◽  
pp. 297-306
Author(s):  
Alexandra Tucă ◽  
Valerian Croitorescu ◽  
Mircea Oprean ◽  
Thomas Brandemeir
Keyword(s):  
Real World ◽  
Input Signal ◽  
Interaction Design ◽  
High Frequency ◽  
Driver Behavior ◽  
Low Frequency ◽  
Driving Simulators ◽  
Sensory Inputs ◽  
Frequency Information ◽  
Busy Road

AbstractThe interaction human-vehicle, as well as driver’s behavior are subject long debated in the automotive engineering domain. Driving simulators have an extraordinary important role allowing research that would not be possible to study in real world scenarios.A driver uses his sensory inputs to obtain the required input to base his decision on. The bandwidth of the required input signal should be in accordance to the driver’s task. For simple tasks, like turning on the screen wipers or direction indicator, low frequency information is sufficient. High frequency information is required when cornering on a busy road or when driving in relatively limit situations.The optimal configuration of each sub-system remains a significant cause for debate and still poses a major challenge when considering the ability of simulators to extract realistic driver behavior. If a difference is observed between real and virtual conditions, the factors specifically cause these differences are very difficult to be explained.

At-Risk Driving Behavior in Drivers with Diabetes: A Neuroergonomics Approach

2017 ◽  
Vol 61 (1) ◽  
pp. 1881-1885
Author(s):  
Jennifer Merickel ◽  
Robin High ◽  
Lynette Smith ◽  
Chris Wichman ◽  
Emily Frankel ◽  
...  
Keyword(s):  
At Risk ◽  
Real World ◽  
Driver Behavior ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Continuous Glucose Monitor ◽  
Glucose Levels ◽  
Driver Performance ◽  
Safety Interventions ◽  
And Behavior

This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 drivers (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks with continuous glucose monitor (CGM) wearable sensors. Contemporaneous real-world driving performance and behavior were measured with in-vehicle video and electronic sensor instrumentation packages. Results showed clear links between at-risk glucose levels (particularly hypoglycemia) and changes in driver performance and behavior. DM participants often drove during at-risk glucose levels (low and high) and showed cognitive impairments in key domains for driving, which are likely linked to frequent hypoglycemia. The finding of increased driving risk in DM participants was mirrored in state records of crashes and traffic citations. Combining sensor data and phenotypes of driver behavior can inform patients, caregivers, safety interventions, policy, and design of supportive in-vehicle technology that is responsive to driver state.

The Potential of Subliminal Information Displays to Change Driver Behavior

2014 ◽  
Vol 23 (1) ◽  
pp. 51-70 ◽  
Author(s):  
Andreas Riener ◽  
Pierre Chalfoun ◽  
Claude Frasson
Keyword(s):  
Human Computer Interaction ◽  
Real World ◽  
Driver Behavior ◽  
User Studies ◽  
Interaction Techniques ◽  
Information Displays ◽  
Subliminal Messages ◽  
History Of ◽  
Near Future ◽  
Fuel Costs

In the long history of subliminal messages and perception, many contradictory results have been presented. One group of researchers suggests that subliminal interaction techniques improve human–computer interaction by reducing sensory workload, whereas others have found that subliminal perception does not work. In this paper, we want to challenge this prejudice by first defining a terminology and introducing a theoretical taxonomy of mental processing states, then reviewing and discussing the potential of subliminal approaches for different sensory channels, and finally recapitulating the findings from our studies on subliminally triggered behavior change. Our objective is to mitigate driving problems caused by excessive information. Therefore, this work focuses on subliminal techniques applied to driver–vehicle interaction to induce a nonconscious change in driver behavior. Based on a survey of related work which identified the potential of subliminal cues in driving, we conducted two user studies assessing their applicability in real-world situations. The first study evaluated whether subtle (subliminal) vibrations could promote economical driving, and the second exposed drivers to very briefly flashed visual stimuli to assess their potential to improve steering behavior. Our results suggest that subliminal approaches are indeed feasible to provide drivers with added driving support without dissipating attention resources. Despite the lack of general evidence for uniform effectiveness of such interfaces in all driving circumstances, we firmly believe that such interfaces are valuable since they may eventually prevent accidents, save lives, and even reduce fuel costs and CO2 emissions for some drivers. For all these reasons, we are confident that subliminally driven interfaces will find their way into cars of the (near) future.

Evaluation of driver-behavior models in real-world car-following task

2009 ◽  
Author(s):  
Pongtep Angkititrakul ◽  
Terashima Ryuta ◽  
Toshihiro Wakita ◽  
Kazuya Takeda ◽  
Chiyomi Miyajima ◽  
...  
Keyword(s):  
Real World ◽  
Driver Behavior ◽  
Car Following ◽  
Behavior Models

scholarly journals Neural Networks for Driver Behavior Analysis

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 342
Author(s):  
Fabio Martinelli ◽  
Fiammetta Marulli ◽  
Francesco Mercaldo ◽  
Antonella Santone
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Real World ◽  
Experimental Analysis ◽  
Network Architecture ◽  
Driver Behavior ◽  
Neural Network Architecture ◽  
Real World Data ◽  
Fully Connected

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results.

Modeling of Driver Behavior in Real World Scenarios Using Multiple Noninvasive Sensors

2013 ◽  
Vol 15 (5) ◽  
pp. 1213-1225 ◽  
Author(s):  
Nanxiang Li ◽  
Jinesh J. Jain ◽  
Carlos Busso
Keyword(s):  
Real World ◽  
Driver Behavior ◽  
Noninvasive Sensors

scholarly journals Corrigendum to “Driver behavior and the use of automation in real-world driving” [Accid. Anal. Preven. 158 (2021) 106217]

2021 ◽  
Vol 160 ◽  
pp. 106319
Author(s):  
Pnina Gershon ◽  
Sean Seaman ◽  
Bruce Mehler ◽  
Bryan Reimer ◽  
Joseph Coughlin
Keyword(s):  
Real World ◽  
Driver Behavior

scholarly journals How Long Does It Take to Relax? Observation of Driver Behavior During Real-World Conditionally Automated Driving

2019 ◽  
Author(s):  
Kamil Omozik ◽  
Yucheng Yang ◽  
Isabella Kuntermann ◽  
Sebastian Hergeth ◽  
Klaus Bengler
Keyword(s):  
Real World ◽  
Driver Behavior ◽  
Automated Driving

scholarly journals Driver Speed Profiles Index Warning Signs of MCI

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 127-128
Author(s):  
Jennifer Merickel ◽  
Ruiqian Wu ◽  
Matthew Rizzo ◽  
Ying Zhang
Keyword(s):  
Cognitive Decline ◽  
Real World ◽  
Driver Behavior ◽  
Ground Truth ◽  
Time Of Day ◽  
Warning Signs ◽  
Validation Data ◽  
Age Related ◽  
Behavior Profiles ◽  
Early Warnings

Abstract Goal Use driver behavior profiles to screen and index early warnings of cognitive decline and Alzheimer’s disease (AD). Hypothesis: Real-world driver speed behavior profiles discriminate mild cognitive impairment (MCI). Methods Sensors were installed in personal vehicles of 74 legally-licensed, active drivers (age: 65-90 years, μ = 75.85) who completed 2, 3-month real-world driving assessments, including demographic and cognitive assessments, 1 year apart (244,564 miles driven). MCI status was indexed using 8 neuropsychological tests (spanning executive function, visuospatial skills, processing speed, and memory), relevant to MCI and driving. Driving environment was indexed from state speed limit (SL; roadway type: residential, commercial, interstate) and sunrise-sunset databases (time of day: day vs. night). Models: Data were randomly split into training (66%) and validation (33%) sets. An optimal mixed effects logistic regression model was determined from validation data AUC values. Results MCI drivers drove slower with optimal discrimination (estimated for every 5 mph decrease in speeding) in 1) residential roads (SL 25-35 mph; MCI odds increased by 6% [95% CI: 2-11%]), 2) interstate roads (SL >55 mph; MCI odds increased by 14% [95% CI: 8-20%]), and 3) night environments (MCI odds increased by 7% [95% CI: 2-12%]). Conclusion Quantitative indices of real-world driver data provide “ground truth” for screening and indexing phenotypes of cognitive decline, in line with ongoing efforts to link driver behavior with age-related cognitive decline and AD biomarkers. Behavioral biomarkers for diagnosing early warnings of dementia could ultimately bolster our ability to detect and intervene in early AD.

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