Vision Based Driver Assistance Systems

Abstract—In this work, we introduce a new approach for automated risk estimation of source disclosure in tactical coalition operation environments. Our approach seeks to mitigate the unintended disclosure of data sources (ie sensors) and sensor capabilities through the selective release of information to other parties in a tactical network. While the proposed approach can be used for the automatic filtering of information based on pre-defined levels of acceptable risk, we introduce an application where the risk estimation is defined in ...

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18-28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle framemounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-Contents lists available at ScienceDirect

Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are promising markers of continuous driver alertness in laboratory and track studies, however their ability to determine fitness-to-drive in naturalistic driving is unknown. This study assessed the efficacy of a predrive ocular assessment for predicting sleep-related impairment in naturalistic driving, in rotating shift workers. Fifteen healthcare workers drove an instrumented vehicle for 2 weeks, while working a combination of day, evening and night shifts. The vehicle monitored lane departures and behavioural microsleeps (blinks > 500 ms) during the drive. Immediately prior to driving, ocular parameters were assessed with a 4-min test. Lane departures and behavioural microsleeps occurred on 17.5 % and 10 % of drives that had pre-drive assessments, respectively. Pre-drive blink duration significantly predicted behavioural microsleeps and showed promise for predicting lane departures (AUC = 0.79 and 0.74). Pre-drive percentage of time with eyes closed had high accuracy for predicting lane departures and behavioural microsleeps (AUC = 0.73 and 0.96), although was not statistically significant. Pre-drive psychomotor vigilance task variables were not statistically significant predictors of lane departures. Self-reported sleep-related and hazardous driving events were significantly predicted by mean blink duration (AUC = 0.65 and 0.69). Measurement of ocular parameters pre-drive predict drowsy driving during naturalistic driving, demonstrating potential for fitness-to-drive assessment in operational environments.

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including ...

Numerous measures shown to depict reliably the developing drowsy state include changes in human electroencephalographic (EEG) output, 10,11 electrooculography (EOG) or other oculographic measurements, 7,12,13 heart rate variability, 14-16 and neurobehavioral measures of attention and vigilance (i.e., psychomotor vigilance task). 6,17,18 While these measurements are sensitive to drowsiness, they can present numerous problems: (1) most measures require substantial off-line processing, especially EEG/EOG; (2) performance measures present a snapshot Study Objectives: Numerous ocular parameters have been proposed as reliable physiological markers of drowsiness. A device that measures many of these parameters and then combines them into a single metric (the Johns Drowsiness Scale [JDS]) is being used commercially to assess drowsiness in professional drivers. Here, we examine how these parameters refl ect changes in drowsiness, and how they relate to objective and subjective indices of the drowsy state in a controlled laboratory setting. Design: A within subject prospective study. Participants: 29 healthy adults (18 males; mean age 23.3 ± 4.6 years; range 18-34 years) Interventions: N/A. Measurements and Results: Over the course of a 30-h extended wake vigil under constant routine (CR) conditions, participants were monitored using infrared refl ectance oculography (Optalert) and completed bi-hourly neurobehavioral tests, including the Karolinska Sleepiness Scale (KSS) and Psychomotor Vigilance Task (PVT). Ocular-defi ned increases in drowsiness were evident with extended time awake and during the biological night for all ocular parameters; JDS being the most sensitive marker of drowsiness induced by sleep regulatory processes (p < 0.0001). In addition, the associations between JDS in the preceding 10-min period and subsequent PVT lapses and KSS were stronger (AUC 0.74/0.80, respectively) than any other ocular metric, such that PVT lapses, mean response time (RT), and KSS increased in a dose-response manner as a function of prior JDS score (p < 0.0001). Conclusions: Ocular parameters captured by infrared refl ectance oculography detected fl uctuations in drowsiness due to time awake and during the biological night. The JDS outcome was the strongest predictor of drowsiness among those tested, and showed a clear association to objective and subjective measures of drowsiness. Our fi ndings indicate this real-time objective drowsiness monitoring system is an effective tool for monitoring changes in alertness and performance along the alert-drowsy continuum in a controlled laboratory setting.

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18-28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle framemounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-Contents lists available at ScienceDirect

The growing popularity and development of data mining technologies bring serious threat to the security of individual's sensitive information. An emerging research topic in data mining, known as privacypreserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Number of accidents during driving is increasing day by day and drowsy driving has been implicated as a causal factor in many accidents. Goal of driver drowsiness detection systems is to reduce these accidents. It has been seen that most of the accidents occur due to driver's fatigue and a small due to inattention factor, therefore this paper reviews driver's fatigue monitoring techniques in detail with a little overview of others also.

Background: Pilot impairment due to drowsiness and fatigue is a major contributing factor in aviation accidents world over. There has been a keen interest in developing a system that can monitor and quantify drowsiness, as well as provide a real-time warning to the operator. Optalert ® Eagle Drowsiness Monitoring system in an unobtrusive method to evaluate the level of drowsiness and provides a real time score of the same. Aim and objectives: To evaluate the Optalert ® Eagle Drowsiness Monitoring system as a tool for assessment of fatigue due to sleep loss. Methodology: The study subjected 12 volunteers to sleep loss of two different durations and analysed the effects of sleep loss using the John's Drowsiness Scale (JDS) derived from Optalert ® Eagle Drowsiness Monitoring system, the Reaction Time using the Psychomoter Visual Tracking (PVT) and the Sleepiness score from Stanford Sleepiness Scale (SSS). Results: The subjective drowsiness arising in a "2 hours sleep loss followed by a period of restful sleep" was not detected by by Optalert® Eagle Drowsiness Monitoring system. However, the system detected the drowsiness arising after "20 hours of continuous wakefulness". The onset of performance decrement and subjective feeling of sleepiness is corroborated by the PVT and the SSS scores. Conclusion: The Optalert® Eagle Drowsiness Monitoring system is an objective measure for detecting drowsiness. The increase in scores correlates well with other objective and subjective criteria. It is a non-intrusive measure for detecting drowsiness and therefore can be used in operational scenarios after field trials.

With the rising concern about the needs of people with physical disabilities and with the aging of the population there is a major concern of creating electronic devices that may improve the life of the physically handicapped and elderly person. One of these new solutions passes through the adaptation of electric wheelchairs in order to give them environmental perception, more intelligent capabilities and more adequate Human–Machine Interaction. This paper focuses in the development of a user-friendly multimodal ...

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

One of the most important tasks in hand recognition applications for human-robot interaction is hand segmentation. This work presents a method that uses a Microsoft Kinect camera, for hand localization and segmentation. One important aspect for robot control besides hand localization is hand orientation, which is used in this work to control robot heading direction (left or right) and linear velocity. The system first calculates hand position, and then a kalman filter is used to estimate displacement and linear velocity in a smoother way. Experimental results show that the system is easy to use, and can be applied on several different humancomputer interface applications.

The article provides a study of driver fatigue experimental research on interactive driver simulator. Visually available face features, movements of eyes and facial expression are followed with the help of distant eye tracker. The driver behaviour of sleep deprived participants is observed and compared to that of the rested drivers. This research targets sparing the key features of driver behaviour for further implementation in detection methods of driver fatigue in the human-machine interface of modern and future cars.

One of the most important tasks in hand recognition applications for human-robot interaction is hand segmentation. This work presents a method that uses a Microsoft Kinect camera, for hand localization and segmentation. One important aspect for robot control besides hand localization is hand orientation, which is used in this work to control robot heading direction (left or right) and linear velocity. The system first calculates hand position, and then a kalman filter is used to estimate displacement and linear velocity in a smoother way. Experimental results show that the system is easy to use, and can be applied on several different human-computer interface applications.

One of the most important tasks in hand recognition applications for human-robot interaction is hand segmen- tation. This work presents a method that uses a Microsoft Kinect camera, for hand localization and segmenta- tion. One important aspect for robot control besides hand localization is hand orientation, which is used in this work to control robot heading direction (left or right) and linear velocity. The system first calculates hand po- sition, and then a kalman filter is used to estimate displacement and linear velocity in a smoother way. Exper- imental results show that the system is easy to use, and can be applied on several different human-computer interface applications.

To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving. This systematic review searched the following electronic databases: Medline, Science direct, Scopus, EMBASE, PsycINFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies. Of 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in t...

With the rising concern about the needs of people with physical disabilities and with the aging of the population there is a major concern of creating electronic devices that may improve the life of the physically handicapped and elderly person. One of these new solutions passes through the adaptation of electric wheelchairs in order to give them environmental perception, more intelligent capabilities and more adequate Human -Machine Interaction. This paper focuses in the development of a user-friendly multimodal interface, which is integrated in the Intellwheels project. This simple multimodal human-robot interface developed allows the connection of several input modules, enabling the wheelchair control through flexible input sequences of distinct types of inputs (voice, facial expressions, head movements, keyboard and, joystick). The system created is capable of storing user defined associations, of input's sequences and corresponding output commands. The tests performed have proved the system efficiency and the capabilities of this multimodal interface.

Road traffic is known to have its own complex dynamics. One implication of complexity is that road traffic collisions have become an unwelcome but unavoidable part of human life. One of the major causes of collisions is the human factor. While car manufacturers have been focusing on developing feasible solutions for autonomous and semi-autonomous vehicles to replace or assist human drivers, the proposed solutions have been designed only for individual vehicles. The road traffic, however, is an interaction-oriented system including complex flows. Such a system requires a complex systems approach to solving this problem as it involves considering not only pedestrians, road environment, but also road traffic which can include multiple vehicles. Recent research has demonstrated that large-scale autonomous vehicular traffic can be better modeled using a collective approach as proposed in the form of vehicular cyber-physical systems (VCPS) such as given by Li et al. (IEEE Trans Parallel Distrib Syst 23(9):1775–1789, 2012) or Work et al. (Automotive cyber physical systems in the context of human mobility. In: National workshop on high-confidence automotive cyber-physical systems, Troy, MI, 2008).  To the best of our knowledge, there is currently no comprehensive review of collision avoidance in the VCPS. In this paper, we present a comprehensive literature review of VCPS from the collision-avoidance perspective. The review includes a careful selection of articles from highly cited sources presented in the form of taxonomy. We also highlight open research problems in this domain.

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

Current Knowledge/Study Rationale: Objective indicators of drowsiness have the potential to identify drowsy drivers. This study evaluated the accuracy of ocular measures for detecting drowsiness-related behavioral impairment in real-time. Study Impact: Several ocular measures, particularly those measuring the average duration of episodes of eye closure, were found to be accurate in the real-time detection of behavioral impairment in the laboratory. Ocular measures are promising indicators of real-time drowsiness.

Two of the leading causes of automobile accidents are driver impairment due to alcohol and drowsiness. Apparently, a relatively large percentage of these accidents occur because drivers are unaware of the degree to which they are impaired. The purpose of this research was to develop models, utilizing changes in driver behavior, which could detect driver impairment due to alcohol, drowsiness, or the combination of alcohol and drowsiness, and which could be practically implemented in an automobile. A computer-controlled automobile simulator was used to simulate a nighttime highway driving scenario for six drivers who participated in each of four conditions: a control condition, an alcohol condition, a sleep-deprived condition, and a combined alcohol and sleep-deprived condition. The results indicated that a useful on-board drowsiness detection device is possible and practical for highway driving. The results also showed that on-board alcohol impairment detection may be possible at levels below the legal driving limit in most states (BAC 0.1%).

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

One of the most important tasks in hand recognition applications for human-robot interaction is hand segmen- tation. This work presents a method that uses a Microsoft Kinect camera, for hand localization and segmenta- tion. One important aspect for robot control besides hand localization is hand orientation, which is used in this work to control robot heading direction (left or right) and linear velocity. The system first calculates hand po- sition, and then a kalman filter is used to estimate displacement and linear velocity in a smoother way. Exper- imental results show that the system is easy to use, and can be applied on several different human-computer interface applications.

Question of the study

The drowsiness of drivers is believed to be a major cause of road crashes, but there is no standardized method for determining how drowsy a driver is or was at a particular time. This report describes how drowsiness, measured on a new scale (the Johns Drowsiness Scale or JDS) based on a weighted combination of ocular variables measured by infrared reflectance oculography, was related to performance failures in two different kinds of reaction-time (RT) tests as well as during simulated driving tests.
Methods

31 healthy volunteers performed simple (SRT) and choice (CRT) RT tests for 15 minutes with and without sleep deprivation for 27–33 hours. Their drowsiness was measured as a JDS score (0–10) each minute. In a separate experiment, 15 healthy young adults simulated driving in a car for about 70 minutes when alert and when sleep-deprived while their drowsiness was also measured.
Results

After sleep deprivation, RTs increased and errors of omission (failure to respond within 2 seconds from the start of the stimulus) occurred more frequently in both the SRT and CRT tests, the frequency of driving off the road increased, and JDS scores also increased significantly. The risk per minute for each subject of making an error of omission in SRT tests and of driving “off road” in a car simulator increased progressively with JDS scores (p < 0.001).
Conclusions

The risk of performance failure in the drowsy state is associated with high JDS scores, mainly reflecting changes in the characteristics of eyelid movements during blinks at the time. This could form the basis of a new method for continuously assessing the risks of drowsiness while driving.

Fragestellung: Schläfrigkeit am Steuer ist eine Hauptursache für Autounfälle, sie kann jedoch nicht genau erfasst werden. Es wird ein neues System mit Infrarot (IR) Reflektionsokulographie beschrieben, welches mit Sensoren an einem Brillengestell die Schläfrigkeit des Fahrers kontinuierlich auf einer neuen Skala (IDS) bestimmt. Methode: Bei 50 Probanden wurden Augen- und Augenlid-Bewegungen gemessen, während sie Reaktionszeit-Tests im wachen und schläfrigen Zustand durchführten. Auf Parameter, die für Augenbewegungen berechnet wurden, wurde eine multiple Regression angewandt und für jede Minute berechnet. Diese diente der Entwicklung einer neuen Schläfrigkeitsskala (IDS). Wenn im Fahrsimulator von der Straße heruntergefahren wurde, war dies ein Zeichen gefährlichen Fahrens unter Schlafentzug. Ergebnisse: Die multiple Regression ergab eine hoch signifikante Vorhersage der Schläfrigkeit (r = 0.70; p < 0.0001). Der mittlere IDS Wert und die mittlere Reaktionszeit korrelierten unter allen Testbedingungen hoch (r = 0.70; n = 88; p < 0.001). Acht schläfrige Fahrzeugführer fuhren insgesamt 62-mal von der Straße, 61 Ereignisse konnten durch einen IDS Wert über 5 erkannt werden. Schlussfolgerung: Die Schläfrigkeit des Fahrers kann kontinuierlich mittels IR Reflektionsokulographie zusammen mit der neuen Schläfrigkeitsskala IDS bestimmt werden. Eine individuelle Anpassung des Verfahrens ist ebenso wenig erforderlich wie das Anlegen von Elektroden. Question of the study Drowsy driving is believed to be a major factor in road crashes, but currently cannot be assessed accurately. A new system of infrared (IR) reflectance oculography is described that uses transducers attached to a glasses frame to measure drivers' drowsiness continuously on a new scale (JDS). Driving in a car simulator was investigated in relation to JDS scores per minute. Methods Fifty volunteers had their eye and eyelid movements monitored while performing RT-tests when alert and when drowsy. Multiple regression analysis of ocular variables, measured every minute in alert and drowsy conditions, was used to establish the drowsiness scale (JDS). Driving off the road in the driving simulator was the criterion for dangerous driving by 8 sleep-deprived drivers. Results The regression predicting conditions was highly significant (R=0.70, p<0.0001). Mean JDS scores and mean RTs in all test conditions were highly correlated (r=0.70, n=88, p<0.001). There were 62 "off-road" events in 8 drowsy drivers, and 61 of them were preceded by JDS scores > 5. Conclusions It is possible to measure drivers’ drowsiness continuously by IR oculography using the new JDS scale which does not require adjustment for individuals or the attachment of electrodes.

Driver drowsiness is a major cause of severe accidents, many of which involve a single vehicle lane departure. The objective of the experiment described in this paper is to determine the relationships between drowsiness, lane departure events (LDE) and effects of a warning system. While in case of driver distraction the impact of such a warning system can be tested in real traffic, for reasons of safety (and reproducibility), a laboratory-based driving simulator is being used in this project. The experiments were conducted with a cohort of 63 healthy male subjects aged 22 to 27 driving for about 2.5 hrs in a stimuli-deprived scenario with a six-fold repetition under carefully controlled conditions. Several hundreds micro-sleep episodes were identified in the 53 successful trials by electrooculogram and video signal and confirmed by behavioral analysis; more than 800 lane departure warnings (LDW) occurred in the assisted sub-cohort of 17 drivers. A combined analysis of the LDE with and without LDW shows significant reduction in number, time, departure length and out-of-lane area for the assisted subjects. The timing and design of the warning could furthermore prevent almost 85% of the lane departure events caused by sleepiness.

With the rising concern about the needs of people with physical disabilities and with the aging of the population there is a major concern of creating electronic devices that may improve the life of the physically handicapped and elderly person. One of these new solutions passes through the adaptation of electric wheelchairs in order to give them environmental perception, more intelligent capabilities and more adequate Human–Machine Interaction.