Deep Learning Based Driver Behavior Modeling And Analysis
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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.
Proceedings of 17th International Conference on Machine Learning and Computing
This book comprises original and peer reviewed research papers presented at 2025 17th International Conference on Machine Learning and Computing that was held in Guangzhou, China, from February 14 to 17, 2025. The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. Topics discussed in this volume include Machine Learning Theory and Algorithms, High-performance Computing Models and Data Processing, Large-scale Language Models and Natural Language Processing, Data-oriented Information System Optimization and Intelligent Computing, AI-based Intelligent Control Systems and System Security, etc. The book will become a valuable resource for academics, industry professionals, and engineers working in the related fields of machine learning and computing.
Human-like Decision-making and Control for Automated Driving
The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety. Human-like Decision-making and Control for Automated Driving takes a step forward to address breaking through the limitation of future automation applications, investigating in depth: Human driving feature modeling and analysis Personalized motion control for AVs Human-like decision making for AVs Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2022005