Driver fatigue is responsible for up to 30% of fatal car accidents. This issue has been addressed by many scholars in order to save thousands of lives and reduce many costs. The goal of this work is to reduce the number of car accidents caused by mental fatigue or drowsiness. In order to achieve this goal, we propose a personalized Bayesian Network (BN) to detect driver’s fatigue. The detection of driver fatigue is enhanced by combining data that reflects the driver’s performance with context-aware information. The parameters of the system are the angular velocity of the steering wheel, the pressure applied to the gas and brake pedals, the grip force on the steering wheel, weather conditions, current traffic, and time of day. The aforementioned parameters of the network are updated on a regular basis, which makes fatigue detection more reliable. Besides, these parameters allow the system to detect a driver’s fatigue through driving performance which is both individualized and context aware. In our experiment, subjects drove a driving simulator game during six sessions, for a total of one hour. After each session, every subject used the Karolinska Sleepiness Scale (KSS) to rate her fatigue’s level. The system was trained on the data collected separately from each user, allowing BN to be personalized for each subject. The proposed system showed an average accuracy of 96%, and ability to overcome the issue of individual differences and uncertainties which are involved in fatigue detection process.

Driver fatigue is responsible for up to 30% of fatal car accidents. This issue has been addressed by many scholars in order to save thousands of lives and reduce many costs. The goal of this work is to reduce the number of car accidents caused by mental fatigue or drowsiness. In order to achieve this goal, we propose a personalized Bayesian Network (BN) to detect driver’s fatigue. The detection of driver fatigue is enhanced by combining data that reflects the driver’s performance with context-aware information. The parameters of the system are the angular velocity of the steering wheel, the pressure applied to the gas and brake pedals, the grip force on the steering wheel, weather conditions, current traffic, and time of day. The aforementioned parameters of the network are updated on a regular basis, which makes fatigue detection more reliable. Besides, these parameters allow the system to detect a driver’s fatigue through driving performance which is both individualized and context aware. In our experiment, subjects drove a driving simulator game during six sessions, for a total of one hour. After each session, every subject used the Karolinska Sleepiness Scale (KSS) to rate her fatigue’s level. The system was trained on the data collected separately from each user, allowing BN to be personalized for each subject. The proposed system showed an average accuracy of 96%, and ability to overcome the issue of individual differences and uncertainties which are involved in fatigue detection process.

Two experiments were conducted to study driver fatigue. The first investigated driver fatigue and driving performance. Thirty one Participants completed a questionnaire to obtain their Subjective Driver Fatigue Score (SDFS) quantifying fatigue levels. Driving performance was evaluated by measuring steering wheel, lateral position, etc. The results showed significant increases in the SDFS and driving performance impairment following simulated driving sessions. Further analysis suggested a linear relationship between the SDFS and the standard deviation of lateral acceleration. Subjective fatigue assessment and driving performance were plotted as radar diagrams to show the multidimensional characteristics. The second experiment examined effects of caffeine and music on the SDFS, driving performance, and 8 EEG signal parameters. Initially, there was no significant inter-sessional variation in the dependent variables, suggesting all sessions were started at similar states. The final SDFS for caffeine and music sessions were significantly lower than control sessions, suggesting both inhibited subjective fatigue increase. Driving performance deteriorated less significantly in caffeine sessions than in control and music sessions. The results suggested that caffeine was more effective than music. EEG was not changed significantly. However, the amplitude of α wave increased significantly for an extremely fatigued individual, along with vehicle drifting and micro-sleep. In conclusion, the SDFS developed in this study successfully estimated subjective driver fatigue levels and showed a linear relationship with driving performance during driving tasks. Caffeine and music reduced driver fatigue subjectively similarly, but caffeine also helped subjects maintain driving performance. / Thesis / Doctor of Philosophy (PhD) / In this project, two experiments were conducted to study driver fatigue. A subjective driver fatigue score was specially developed and used as a driver fatigue indicator. This score was sensitive to driver fatigue changes, and showed a linear relationship with the standard deviation of lateral acceleration. Two popular driver fatigue countermeasures, caffeine and music, were examined to investigate the effects on subjective driver fatigue and driving performance. The results showed that caffeine reduced subjective driver fatigue and helped driver maintain good driving performance; however, music only helped drivers reduce subjective driver fatigue.

<p>Many traffic accidents are caused by driver unawareness. This includes fatigue, drowsiness and distraction. In this thesis two systems are described that could be used to decrease the number of accidents. In the first part of this thesis a system using long-term information to warn drivers suffering from fatigue is developed. Three different versions with different criteria are evaluated. The systems are shown to handle more then 60% of the cases correctly.</p><p>The second part of this thesis examines the possibilities of developing a warning system based on the predicted time-to-lane crossing, TLC. A basic TLC model is implemented and evaluated. For short time periods before lane crossing this may offer adequate accuracy. However the accuracy is not good enough for the model to be used in a TLC based warning system to warn the driver of imminent lane departure.</p>

Many traffic accidents are caused by driver unawareness. This includes fatigue, drowsiness and distraction. In this thesis two systems are described that could be used to decrease the number of accidents. In the first part of this thesis a system using long-term information to warn drivers suffering from fatigue is developed. Three different versions with different criteria are evaluated. The systems are shown to handle more then 60% of the cases correctly. The second part of this thesis examines the possibilities of developing a warning system based on the predicted time-to-lane crossing, TLC. A basic TLC model is implemented and evaluated. For short time periods before lane crossing this may offer adequate accuracy. However the accuracy is not good enough for the model to be used in a TLC based warning system to warn the driver of imminent lane departure.

The automotive industry is growing bigger each year. The central concern for any automotive company is driver and passenger safety. Many automotive companies have developed driver assistance systems, to help the driver and to ensure driver safety. These systems include adaptive cruise control, lane departure warning, lane change assistance, collision avoidance, night vision, automatic parking, traffic sign recognition, and driver fatigue detection. In this thesis, we aim to build a driver fatigue detection system that advances the research in this area. Using vision in detecting driver fatigue is commonly the key part for driver fatigue detection systems. We have decided to investigate different direction. We examine the driver's voice, heart rate, and driving performance to assess fatigue level. The system consists of three main modules: the audio module, the heart rate and other signals module, and the Bayesian network module. The audio module analyzes an audio recording of a driver and tries to estimate the level of fatigue for the driver. A Voice Activity Detection (VAD) module is used to extract driver speech from the audio recording. Mel-Frequency Cepstrum Coefficients, (MFCC) features are extracted from the speech signal, and then Support Vector Machines (SVM) and Hidden Markov Models (HMM) classifiers are used to detect driver fatigue. Both classifiers are tuned for best performance, and the performance of both classifiers is reported and compared. The heart rate and other signals module uses heart rate, steering wheel position, and the positions of the accelerator, brake, and clutch pedals to detect the level of fatigue. These signals' sample rates are then adjusted to match, allowing simple features to be extracted from the signals, and SVM and HMM classifiers are used to detect fatigue level. The performance of both classifiers is reported and compared. Bayesian networks' abilities to capture dependencies and uncertainty make them a sound choice to perform the data fusion. Prior information (Day/Night driving and previous decision) is also incorporated into the network to improve the final decision. The accuracies of the audio and heart rate and other signals modules are used to calculate certain CPTs for the Bayesian network, while the rest of the CPTs are calculated subjectively. The inference queries are calculated using the variable elimination algorithm. For those time steps where the audio module decision is absent, a window is defined and the last decision within this window is used as a current decision. The performance of the system is assessed based on the average accuracy per second. A dataset was built to train and test the system. The experimental results show that the system is very promising. The performance of the system was assessed based on the average accuracy per second; the total accuracy of the system is 90.5%. The system design can be easily improved by easily integrating more modules into the Bayesian network.

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While driver distraction has been extensively studied in laboratory and empirical field studies, the prevalence of driver distraction on our nation's highways and the relative crash risk is unknown. It has recently become technologically feasible to conduct unobtrusive large-scale naturalistic driving studies as the costs and size of computer equipment and sensor technology have both dramatically decreased. A large-scale naturalistic driving study was conducted using 100 instrumented vehicles (80 privately-owned and 20 leased vehicles). This data collection effort was conducted in the Washington DC metropolitan area on a variety of urban, suburban, and rural roadways over a span of 12-13 months. Five channels of video and kinematic data were collected on 69 crashes and 761 near-crashes during the course of this data collection effort. The analyses conducted here are the first to establish direct relationships between driving inattention and crash and near-crash involvement. Relative crash risk was calculated using both crash and near-crash data as well as normal, baseline driving data, for various sources of inattention. Additional analyses investigated the environmental conditions drivers choose to engage in secondary tasks or drive fatigued, assessed whether questionnaire data were indicative of an individual's propensity to engage in inattentive driving, and examined the impact of driver's eyes off the forward roadway. The results indicated that driving inattention was a contributing factor in 78% of all crashes and 65% of all near-crashes. Odds ratio calculations indicated that fatigued drivers have a 4 times higher crash risk than alert drivers. Drivers engaging in visually and/or manually complex tasks are at 7 times higher crash risk than alert drivers. There are specific environmental conditions in which engaging in secondary tasks or driving fatigued is deemed to be more dangerous, including intersections, wet roadways, undivided highways, curved roadways, and driving at dusk. Short, brief glances away from the forward roadway for the purpose of scanning the roadway environment (e.g., mirrors and blind spots) are safe and decrease crash risk, whereas such glances that total more than 2 seconds away from the forward roadway are dangerous and increase crash risk by 2 times over that of more typical driving. / Ph. D.

Modern vehicles are designed to protect occupants in the event of a crash with some vehicles better at this than others. However, passenger protection during an accident has shown to be not enough in many high impact crashes. Statistics have shown that the human error is the number one contributor to road accidents. This research study explores how driver error can be reduced through technology which observes driver behaviour and reacts when certain unwanted patterns in behaviour have been detected. Finally a system that detects driver fatigue and driver distraction has been developed using non-invasive machine vision concepts to monitor observable driver behaviour. / Electrical Engineering / M. Tech. (Electrical Engineering)