The Evolution of Driver Monitoring Systems: A Shortened Story on Past, Current and Future Approaches How Cars Acquire Knowledge About the Driver's State

2020 ◽  
Author(s):  
Dietrich Manstetten ◽  
Frank Beruscha ◽  
Hans-Joachim Bieg ◽  
Fanny Kobiela ◽  
Andreas Korthauer ◽  
...  
Keyword(s):  
Monitoring Systems ◽  
Driver Monitoring

scholarly journals Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3836
Author(s):  
António Lobo ◽  
Sara Ferreira ◽  
António Couto
Keyword(s):  
Short Distance ◽  
Monitoring Systems ◽  
Mobility Patterns ◽  
Long Distance ◽  
Road Crashes ◽  
Driving Time ◽  
Driver Monitoring ◽  
Driver Inattention ◽  
Ordered Probit Models ◽  
Breaking Time

Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

Average heart rate for driver monitoring systems

2016 ◽  
Vol 4 (3/4) ◽  
pp. 282
Author(s):  
J.M. Cooper ◽  
F. Biondi ◽  
D.L. Strayer ◽  
J.R. Coleman
Keyword(s):  
Heart Rate ◽  
Monitoring Systems ◽  
Average Heart Rate ◽  
Driver Monitoring

Face Pose Estimation From Rigid Face Landmarks For Driver Monitoring Systems

2017 ◽  
Vol 2017 (19) ◽  
pp. 83-88 ◽  
Author(s):  
Bhawani Shankar ◽  
Dakala Jayachandra ◽  
Kalyan Kumar Hati
Keyword(s):  
Pose Estimation ◽  
Monitoring Systems ◽  
Driver Monitoring

Time-To-Collision: A Sensitive Measure of Driver Interaction with Traffic in a Simulated Driving Task

1997 ◽  
Vol 41 (2) ◽  
pp. 1028-1031 ◽  
Author(s):  
Dary D. Fiorentino ◽  
Zareh Parseghian
Keyword(s):  
Real Time ◽  
Performance Criteria ◽  
Monitoring Systems ◽  
Blood Alcohol ◽  
Simulated Driving ◽  
Time To Collision ◽  
Driver Monitoring ◽  
Driving Task ◽  
Simulated Driving Task ◽  
Minimum Performance

In the future, on-board driver monitoring systems could use time-to-collision (TTC) metric algorithms as a real-time measure of driving performance, and alert the driver if performance falls below minimum performance criteria. Such monitoring systems remain years away, but it is currently possible to measure TTC in a simulator. This paper discusses a study to determine whether TTC varies as a function of driver impairment in a simulated driving task. Alcohol was administered to eleven participants, and TTC measures were obtained at 0.00%, 0.04% and 0.08% blood alcohol concentrations (BAC). The results support use of the median TTC, which varied as a function of BAC, as a measure of in-traffic maneuvering performance.

scholarly journals Driver Monitoring Systems: Perceived Fairness of Consequences when Distractions are Detected

2021 ◽  
Author(s):  
Michael A. Nees
Keyword(s):  
Perceived Risk ◽  
Online Survey ◽  
Driver Distraction ◽  
Monitoring Systems ◽  
Perceived Fairness ◽  
Automated Driving ◽  
Safety Feature ◽  
Driver Monitoring ◽  
Versus Level ◽  
Level 2

Driver monitoring may become a standard safety feature to discourage distraction in vehicles with or without automated driving functions. Research to date has focused on technology for identifying driver distraction—little is known about how drivers will respond to monitoring systems. An exploratory online survey assessed the perceived risk and reasonableness associated with driving distractions as well as the perceived fairness of potential consequences when a driver monitoring system detects distractions under either manual driving or Level 2 automated driving. Although more re- search is needed, results suggested: (1) fairness was associated with perceived risk; (2) alerts generally were viewed as fair; (3) more severe consequences (feature lockouts, insurance reporting, automation lockouts, involuntary takeovers) generally were viewed as less fair; (4) fairness ratings were similar for manual versus Level 2 driving, with some potential exceptions; and (5) perceived risk of distractions was slightly lower with automated driving.

scholarly journals Heart rate from face videos under realistic conditions for advanced driver monitoring

2017 ◽  
Vol 3 (2) ◽  
pp. 483-487 ◽  
Author(s):  
Christian S. Pilz ◽  
Sebastian Zaunseder ◽  
Ulrich Canzler ◽  
Jarek Krajewski
Keyword(s):  
Heart Rate ◽  
Velocity Model ◽  
Space Representation ◽  
Monitoring Systems ◽  
Inference Problem ◽  
State Space Representation ◽  
Canonical State ◽  
Driver Monitoring ◽  
Ito Process ◽  
Human Faces

AbstractThe role of physiological signals has a large impact on driver monitoring systems, since it tells something about the human state. This work addresses the recursive probabilistic inference problem in time-varying linear dynamic systems to incorporate invariance into the task of heart rate estimation from face videos under realistic conditions. The invariance encapsulates motion as well as varying illumination conditions in order to accurately estimate vitality parameters from human faces using conventional camera technology. The solution is based on the canonical state space representation of an Itô process and a Wiener velocity model. Empirical results yield to excellent real-time and estimation performance of heart rates in presence of disturbing factors, like rigid head motion, talking, facial expressions and natural illumination conditions making the process of human state estimation from face videos applicable in a much broader sense, pushing the technology towards advanced driver monitoring systems.

Driver monitoring systems (DMS): The future of impaired driving management?

2021 ◽  
pp. 1-9
Author(s):  
Amie C. Hayley ◽  
Brook Shiferaw ◽  
Blair Aitken ◽  
Frederick Vinckenbosch ◽  
Timothy L. Brown ◽  
...  
Keyword(s):  
Monitoring Systems ◽  
Impaired Driving ◽  
Driver Monitoring ◽  
The Future

scholarly journals A Review of Heartbeat Detection Systems for Automotive Applications

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6112
Author(s):  
Toshiya Arakawa
Keyword(s):  
Monitoring System ◽  
Turning Point ◽  
Wearable Devices ◽  
Health Conditions ◽  
Detection Methods ◽  
Monitoring Systems ◽  
Automotive Applications ◽  
Detection Systems ◽  
Physical Conditions ◽  
Driver Monitoring

Many accidents are caused by sudden changes in the physical conditions of professional drivers. Therefore, it is quite important that the driver monitoring system must not restrict or interfere with the driver’s action. Applications that can measure a driver’s heartbeat without restricting the driver’s action are currently under development. In this review, examples of heartbeat-monitoring systems are discussed. In particular, methods for measuring the heartbeat through sensing devices of a wearable-type, such as wristwatch-type, ring-type, and shirt-type devices, as well as through devices of a nonwearable type, such as steering-type, seat-type, and other types of devices, are discussed. The emergence of wearable devices such as the Apple Watch is considered a turning point in the application of driver-monitoring systems. The problems associated with current smartwatch- and smartphone-based systems are discussed, as are the barriers to their practical use in vehicles. We conclude that, for the time being, detection methods using in-vehicle devices and in-vehicle cameras are expected to remain dominant, while devices that can detect health conditions and abnormalities simply by driving as usual are expected to emerge as future applications.

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