Deep Learning-based Driver Behavior Modeling and Analysis

Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques

Driver distraction detection using experimental methods and machine learning algorithms.

Driver distraction causes numerous road accidents, which is approximately equal to 25% of the total crashes according to the reports by the National Highway Traffic Safety Administration. Warnings can be helpful to mitigate the risks caused by driver distraction. Previous studies on driver distraction detection have not sufficiently found relevant input features to filter insignificant information, thus limiting the improvement of efficiency. Moreover, the disadvantages of driving simulators and public roads pose a challenge in collecting suitable data for feature identification and comparisons of performance among driver distraction detection algorithms. While the previous research focuses on improving prediction accuracy, shortening the prediction time is critical in giving timely warnings to drivers. This thesis aims at detecting driver distraction, which could provide faster and accurate warnings to drivers. The developed method is implemented by cutting the redundancy and irrelevant information fed to the algorithms and instead selecting suitable algorithms that achieve the balance between the prediction accuracy and prediction time. Moreover, a closed testing field supplies an environment for collecting more accurate information to identify the relevant features and to determine suitable algorithms. In this study, open-source data and experimental data are used. The results show that a balance between the prediction accuracy and the prediction time is achieved by feeding the relevant features and using suitable machine learning algorithms (e.g. Decision Tree). Compared with existing state-of-the-art methods, the prediction accuracy of the method proposed in this study has reached approximately the same level. More importantly, the efficiency has improved, including reduced prediction time and fewer input features. Consequently, less computer storage is used.PhD in Transport System

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner