Information data flow in awake multi-sensor driver monitoring system

Author(s):  
A. Polychronopoulos ◽  
A. Amditis ◽  
E. Bekiaris
2006 ◽  
Author(s):  
Koji Okuda ◽  
Michimasa Itoh ◽  
Bunji Inagaki

2016 ◽  
Vol 40 (3) ◽  
pp. 885-895 ◽  
Author(s):  
Xuanpeng Li ◽  
Emmanuel Seignez

Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.


2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yen-Lin Chen ◽  
Chao-Wei Yu ◽  
Zi-Jie Chien ◽  
Chin-Hsuan Liu ◽  
Hsin-Han Chiang

This study presents an on-road driver monitoring system, which is implemented on a stand-alone in-vehicle embedded system and driven by effective solar cells. The driver monitoring function is performed by an efficient eye detection technique. Through the driver’s eye movements captured from the camera, the attention states of the driver can be determined and any fatigue states can be avoided. This driver monitoring technique is implemented on a low-power embedded in-vehicle platform. Besides, this study also proposed monitoring machinery that can detect the brightness around the car to effectively determine whether this in-vehicle system is driven by the solar cells or by the vehicle battery. On sunny days, the in-vehicle system can be powered by solar cell in places without the vehicle battery. While in the evenings or on rainy days, the ambient solar brightness is insufficient, and the system is powered by the vehicle battery. The proposed system was tested under the conditions that the solar irradiance is 10 to 113 W/m2and solar energy and brightness at 10 to 170. From the testing results, when the outside solar radiation is high, the brightness of the inside of the car is increased, and the eye detection accuracy can also increase as well. Therefore, this solar powered driver monitoring system can be efficiently applied to electric cars to save energy consumption and promote the driving safety.


Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2804 ◽  
Author(s):  
Han ◽  
Tian ◽  
Shi ◽  
Huang ◽  
Li

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one “representative” object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.


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