Development of a model to predict discomfort in a vehicle due to vibration

Human exposure to vehicular vibration can cause sensations (e.g. physical discomfort or annoyance), health issues and safety problems. In industry, several measurement methods have been proposed to improve ride quality and increase the drivers’ or passengers’ expectations in terms of comfort. The measurement and evaluation methods of quantifying whole-body vibration exposure in relation to human comfort and vibration perception are defined by the International Standard ISO 2631-1. This is the most used standard which provides Health guidance caution zones for risk assessment. The human discomfort threshold limits are not given in this standard. Human discomfort, in general, is defined by measurements based on a shaker table and seat combination. These results when used for “in vehicle situations” may not accurately indicate the level of human discomfort in a vehicle. In this thesis, a seated human’s discomfort is quantified in heave, pitch and roll motions using a four-post rig simulator in order to determine a comfort metric. The quantifying and assessment of discomfort are studied in two categories, which are vehicle dynamics with road inputs, and the human response with human perception to vibration. Comfort/discomfort is a subjective variable; therefore the in-situ experiments were performed based on an objective measurement method with a subjective judgement method. The main novel contribution of this thesis is that subjective and dynamic responses of twenty four seated subjects, in a car on the four post rig excitation, exposed to vertical sinusoidal vibration at five magnitudes in heave, pitch and roll motions were taken at Oxford Brookes University. The physical properties of participants such as age, height, and weight were recorded because human sensitivity, perception and threshold levels may be person dependent. The subjective assessment data was developed based on the response of twenty-four seated subjects to vibration in a car on the four post rig which makes this thesis unique in terms of quantifying of human feeling. From the experimental data (RMS acceleration and subjective assessment), a discomfort metric was developed in terms of the cause-effect relations between the road and the human body. Based on the analysis and results, it was observed that the sensitivity to acceleration decreased with decreasing amplitude and increasing frequency. This discomfort metric was applied to a developed analytical model to predict the vibration response. A predictive integrated vehicle-seat-human model was developed to characterize the biodynamic response behaviour of a seated human subject and analyse the influence of vibration transmitted on the human body segments. The transmissibility results from an integrated model and in-situ discomfort curve measurements were combined to develop a human body discomfort model in a car. The discomfort index curves were predicted by combining the modelling study and the experimental results for heave, pitch and roll modes

Investigation of bus passenger discomfort and driver fatigue: An electroencephalography (EEG) approach

Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments. Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments.The variability in the driver’s psychophysiological responses to the duration of driving occurs systematically. The effects appear to be progressive and aligned such that the driving performance was worst during the last 60 minutes of driving for four hours without a break, but better during the first 30 minutes. Data analysis also showed that a pronounced psychophysiological response exists relative to the influence of the road roughness characteristics, the passenger’s postures, and the bus type. Further analysis of passenger discomfort showed that passengers are more strained while in a standing posture than in a seated posture, irrespective of the bus type and the degree of the road’s roughness. The results indicated that passenger comfort deteriorates as the road roughness coefficient increases. Furthermore, the results demonstrated that female passengers express more discomfort/dissatisfaction than males under the same experimental conditions. Therefore, female passengers are more sensitive than males to a deviation from optimal comfort conditions.This study provides opportunities for future research applications of EEG in transport research studies. It also provides a platform for evaluating different Intelligent Transport System (ITS) technologies, particularly passenger’s reactions in autonomous vehicles