An Unsupervised Approach for Automotive Driver Identification

The adoption of on-vehicle monitoring devices allows different entities to gather valuable data about driving styles, which can be further used to infer a variety of information for different purposes, such as fraud detection and driver profiling. In this paper, we focus on the identification of the number of people usually driving the same vehicle, proposing a data analytic work-flow specifically designed to address this problem. Our approach is based on unsupervised learning algorithms working on non-invasive data gathered from a specialized embedded device. In addition, we present a preliminary evaluation of our approach, showing promising driver identification capabilities and a limited computational effort

Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

Cluster Analysis for Driver Aggressiveness Identification

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Driver’s behavior classification in vehicular communication networks for commercial vehicles

Vehicles are becoming more intelligent and connected due to the demand for faster, efficient, and safer transportation. For this transformation, it was necessary to increase the amount of data transferred between electronic modules in the vehicular network since it is vital for an intelligent system’s decision-making process. Hundreds of messages travel all the time in a vehicle, creating opportunities for analysis and development of new functions to assist the driver’s decision. Given this scenario, the dissertation presents the results of research to characterize driving styles of drivers using available information in vehicular communication network. This master thesis focuses on the process of information extraction from a vehicular network, analysis of the extracted features, and driver classification based on the extracted data. The study aims to identify aggressive driving behavior using real-world data collected from five different trucks running for a period of three months. The driver scoring method used in this study dynamically identifies aggressive driving behavior during predefined time windows by calculating jerk derived from the acquired data. In addition, the K-Means clustering technique was explored to group different behaviors into data clusters. Chapter 2 provides a comprehensive overview of the theoretical framework necessary for the successful development of this thesis. Chapter 3 details the process of data extraction from real and uncontrolled environments, including the steps taken to extract and refine the data. Chapter 4 focuses on the study of features extracted from the preprocessed data, and Chapter 5 presents two methods for identifying or grouping the data into clusters. The results obtained from this study have advanced the state-of-the-art of driver behavior classification and have proven to be satisfactory. The thesis addresses the gap in the literature by using data from real and uncontrolled environments, which required preprocessing before analysis. Furthermore, the study represents one of the pioneering studies conducted on commercial vehicles in an uncontrolled environment. In conclusion, this thesis provides insights into the development of driver behavior classification models using real-world data. Future research can build upon the techniques presented in this study and further refine the classification models. The thesis also addresses the threats to validity that were mitigated and provides recommendations for future research