186 research outputs found

    Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

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    Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

    Sitting behaviour-based pattern recognition for predicting driver fatigue

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    The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers

    A Steering Wheel Mounted Grip Sensor: Design, Development and Evaluation

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    Department of Human Factors EngineeringDriving is a commonplace but safety critical daily activity for billions of people. It remains one of the leading causes of death worldwide, particularly in younger adults. In the last decades, a wide range of technologies, such as intelligent braking or speed regulating systems, have been integrated into vehicles to improve safetyannually decreasing death rates testify to their success. A recent research focus in this area has been in the development of systems that sense human states or activities during driving. This is valuable because human error remains a key reason underlying many vehicle accidents and incidents. Technologies that can intervene in response to information sensed about a driver may be able to detect, predict and ultimately prevent problems before they progress into accidents, thus avoiding the occurrence of critical situations rather than just mitigating their consequences. Commercial examples of this kind of technology include systems that monitor driver alertness or lane holding and prompt drivers who are sleepy or drifting off-lane. More exploratory research in this area has sought to capture emotional state or stress/workload levels via physiological measurements of Heart Rate Variability (HRV), Electrocardiogram (ECG) and Electroencephalogram (EEG), or behavioral measurements of eye gaze or face pose. Other research has monitored explicitly user actions, such as head pose or foot movements to infer intended actions (such as overtaking or lane change) and provide automatic assessments of the safety of these future behaviors ??? for example, providing a timely warning to a driver who is planning to overtake about a vehicle in his or her blind spot. Researchers have also explored how sensing hands on the wheel can be used to infer a driver???s presence, identity or emotional state. This thesis extends this body of work through the design, development and evaluation of a steering wheel sensor platform that can directly detect a driver???s hand pose all around a steering wheel. This thesis argues that full steering hand pose is a potentially rich source of information about a driver???s intended actions. For example, it proposes a link between hand posture on the wheel and subsequent turning or lane change behavior. To explore this idea, this thesis describes the construction of a touch sensor in the form of a steering wheel cover. This cover integrates 32 equidistantly spread touch sensing electrodes (11.250 inter-sensor spacing) in the form of conductive ribbons (0.2" wide and 0.03" thick). Data from each ribbons is captured separately via a set of capacitive touch sensor microcontrollers every 64 ms. We connected this hardware platform to an OpenDS, an open source driving simulator and ran two studies capturing hand pose during a sequential lane change task and a slalom task. We analyzed the data to determine whether hand pose is a useful predictor of future turning behavior. For this we classified a 5-lane road into 4 turn sizes and used machine-learning recognizers to predict the future turn size from the change in hand posture in terms of hand movement properties from the early driving data. Driving task scenario of the first experiment was not appropriately matched with the real life turning task therefore we modified the scenario with more appropriate task in the second experiments. Class-wise prediction of the turn sizes for both experiments didn???t show good accuracy, however prediction accuracy was improved when the classes were reduced into two classes from four classes. In the experiment 2 turn sizes were overlapped between themselves, which made it very difficult to distinguish them. Therefore, we did continuous prediction as well and the prediction accuracy was better than the class-wise prediction system for the both experiments. In summary, this thesis designed, developed and evaluated a combined hardware and software system that senses the steering behavior of a driver by capturing grip pose. We assessed the value of this information via two studies that explored the relationship between wheel grip and future turning behaviors. The ultimate outcome of this study can inform the development of in car sensing systems to support safer driving.ope

    A study on objective evaluation of vehicle steering comfort based on driver's electromyogram and movement trajectory

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    The evaluation of driver's steering comfort, which is mainly concerned with the haptic driver–vehicle interaction, is important for the optimization of advanced driver assistance systems. The current approaches to investigating steering comfort are mainly based on the driver's subjective evaluation, which is time-consuming, expensive, and easily influenced by individual variations. This paper makes some tentative investigation of objective evaluation, which is based on the electromyogram (EMG) and movement trajectory of the driver's upper limbs during steering maneuvers. First, a steering experiment with 21 subjects is conducted, and EMG and movement trajectories of the driver's upper limbs are measured, together with their subjective evaluation of steering comfort. Second, five evaluation indices including EMG and movement information are defined based on the measurements from the first step. Correlation analyses are conducted between each evaluation index and steering comfort rating (SCR), and the results show that all of the indices have significant correlations with SCR. Then, an artificial neural network model is devised based on the aforementioned indices and its predicting performance of SCR is demonstrated as acceptable. The results reveal that it may be feasible to establish an objective evaluation approach for vehicle steering comfort

    Physiological-based Driver Monitoring Systems: A Scoping Review

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    A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

    Ergonomics of intelligent vehicle braking systems

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    The present thesis examines the quantitative characteristics of driver braking and pedal operation and discusses the implications for the design of braking support systems for vehicles. After the current status of the relevant research is presented through a literature review, three different methods are employed to examine driver braking microscopically, supplemented by a fourth method challenging the potential to apply the results in an adaptive brake assist system. First, thirty drivers drove an instrumented vehicle for a day each. Pedal inputs were constantly monitored through force, position sensors and a video camera. Results suggested a range of normal braking inputs in terms of brake-pedal force, initial brake-pedal displacement and throttle-release (throttle-off) rate. The inter-personal and intra-personal variability on the main variables was also prominent. [Continues.

    An integrated model for predicting driver’s discomfort while interacting with car seat and car controls

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    A driving task requires physical demands from the driver to operate car controls, while sitting on the car seat. The near static seated posture in a confined space may causes discomfort and fatigue. In Malaysia, fatigue is the third highest contributing factors to road accident, accounting for 15.7%. Fatigue can interfere with concentration while driving the car. When the driver is getting fatigue, it may reduce the performance, and hence increase the risk of road accident. This show that fatigue effect can cause danger to the driver. The four main objectives of this research are: (1) to evaluate driver’s discomfort and performance while engaged with the car seat and car controls based on subjective assessment.; (2) to analyse the pressure interface on the car seat based on different driving positions.; (3) to evaluate the SEMG surface electromyography (SEMG) signal for the muscle activity based on different driving positions and actions.; and (4) to develop integrated model to predict driver’s discomfort while engaged with the car seat and car controls. Eleven test subjects participated in this experiment. The data for this research were collected by using mixed method approach, comprising of the subjective (Visual Analogue Scales, VAS) and objective assessment methods (SEMG and pressure measurement). The VAS was the subjective assessment method used for measuring the car driver’s discomfort perception while engaging with car seat and car controls, namely steering wheel, manual gear and accelerator pedal. The SEMG was used to measure muscle activity for Deltoid Anterior (DA), Gastrocnemius Medial (GM) and Tibialis Anterior (TA) involving two different positions, the closest seated position to the car controls (Position A) and the further seated position from the car controls (Position B). Having done that, the data were analysed by using Temporal and Amplitude Analysis based on Maximal Voluntary Contraction. The SEMG analysis was in accordance to the SEMG for the Non-Invasive Assessment of Muscles recommendation. The pressure mat was used to measure the pressure distribution of the car seat. In addition, the body measurement, consisting of anthropometric dimension and the joint angle were measured in this study. Referring to VAS assessment, subjects feel more discomfort at Position B while operating the steering wheel at 45 turning degree and gear during changing the gear to gear 1. For pedal control, the subjects experienced discomfort at Position A particularly when releasing the pedal. The SEMG’s findings for the steering wheel task showed the DA at Position B with 45 turning degree showed a higher muscle contraction. Changing the gear to Gear 1 at Position B demonstrated the highest Amplitude at the DA. For pedal control, TA depicted the highest muscle contraction in releasing action at Position A, while the GM showed the highest muscle contraction in pressing action at Position B. In terms of pressure distribution measurement, the buttocks part at Position A depicted the highest mean pressure. The regression test was used to determine the level of significance whether the coefficient of working muscle activity can be used as characteristics and predictors for driver’s discomfort. From the results, the prediction model could be developed. The results indicated that integration between the body measurement and pressure interface or muscle activity show a higher R2; car seat (R2 = 0.952), steering (R2 = 0.983), gear (R2 = 0.980), and pedal (R2 = 0.911 and 0.952). Thus, it can be concluded that the prediction on drivers’ discomfort when driving in different conditions produces better results when incorporating the body measurement that is related with the car seat and car controls

    Prediction of drivers’ performance in highly automated vehicles

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    Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

    Driver lane change intention inference using machine learning methods.

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    Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

    The influence of whole-body vibration and axial rotation on musculoskeletal discomfort of the neck and trunk

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    Elements of an individuals occupational exposure, such as their posture can affect their comfort during work, and also their long term musculoskeletal health. Knowledge as to the extent of the influence of particular aspects of the exposures can help in providing guidance on risk evaluation, and direct future technical design focus. In many situations the exposures interact, and even if the effects of individual exposures are understood, the interactions are often less so. This is certainly the case with off-road driving exposures. Specific investigations have focussed on the effects of vibration exposure, resulting in the development of international standards and guidelines on measurement and evaluation of exposure. Consideration of the posture of the operator can be accomplished through postural assessment tools, although none of the currently available methods are developed specifically for use within a vehicle environment. The issues of both the posture of the operator and the seated vibration exposure are particularly apparent in off-road agricultural driving environments, where the driving task dictates that operator is often required to maintain specific postures whilst also exposed to whole-body vibration. In agriculture, many of the tasks require the operator to maintain axially rotated postures to complete the task effectively. The analysis of the combined effects of the axial rotation of the operator and the whole-body vibration exposure has been limited to a few studies within the literature, and is currently poorly understood. The overall aim of the thesis was to assess the influence of axial rotation and whole-body vibration on the musculoskeletal discomfort of the neck and trunk, in order that the true extent of the exposure risk may be evaluated. A field study was conducted to determine the common characteristics of some typical exposures, to provide a basis for the laboratory studies. A survey of expert opinion was conducted, examining the knowledge and experience of experts in assessing the relative influence of axial rotation and whole-body vibration on operators musculoskeletal health. The main investigations of the thesis are focussed in the laboratory, where the objective and subjective effects of axial rotation (static and dynamic) and whole-body vibration were investigated. Objective measures included the investigation of muscular fatigue in response to exposures. The tasks investigated in the field study indicated that the exposures often exceed the EU Physical Agents Exposure Limit Value, and that the axial rotation is a large component of the postures required. The survey of expert opinion concluded that combined exposure to axial rotation and whole-body vibration would increase the risks of lower back pain, and that acknowledgement of combined exposures should be included when assessing for risk. The results of the laboratory studies indicated that the greatest discomfort was present when subjects were exposed to axial rotation in the neck and shoulders. Out of the 8 muscles investigated, at most 6 of the 8 indicated fatigue during an experimental exposure. The muscle group which was affected most by the exposures was the m. trapezius pars decendens. Findings demonstrated that when subjects were exposed to axial rotation and whole-body vibration they indicated discomfort and their muscles fatigued. However, there was poor correlation between the sites of discomfort and the location of muscular fatigue. The discomfort findings suggest that there is an increased risk of discomfort from experiencing axial rotation together with whole-body vibration. Investigations of muscular fatigue do not substantiate this finding
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