Cluster Analysis for Driver Aggressiveness Identification

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Personality traits and beliefs about peers\u2019 on-road behaviors as predictors of adolescents\u2019 moped-riding profiles.

Several efforts aimed at discriminating between different degrees of on-road risky attitudes have been devoted to the identification of personality profiles among young drivers. However, the results are often inconsistent because of the limits of selfreport measures. To overcome these limits, we tried to identify different profiles based on our study participants\u2019 driving performances in a virtual environment and to look for psychological predictors of inclusion in one of three profiles. One-hundred and fourteen inexperienced adolescents were involved in this study, which included two experimental sessions. During the first, before riding along five virtual courses on a moped simulator, participants\u2019 sensation seeking, locus of control, aggressiveness and beliefs about their peers\u2019 on-road behaviors were measured by means of self-report tools. During the second session, the participants drove the simulator along six courses that were different from those faced in the first session. A cluster analysis was run on a wide number of indexes extracted from the participants\u2019 performances to detect different riding profiles. Three profiles emerged (Imprudent, Prudent and Insecure), with specific riding patterns. The profiles also differed in terms of riding safety, assessed by means of the scores automatically given by the simulator to the participants\u2019 performances. Reporting an external locus of control, underestimating peers\u2019 on-road risky behaviors and showing less concern for fate among the possible causes of crashes are predictors that increase the risk of being included in the Imprudent profile. Low levels of dangerous thrill seeking predict inclusion in the Prudent profile, whereas high rates of self-reported anger play a role in discriminating the Insecure riders from the other profiles. The study indicates that it is possible to identify riding profiles with different degrees of on-road safety among inexperienced adolescents by means of simulated road environments. Moreover, inclusion in these profiles is predicted by different patterns of personality variables and beliefs. Further research is needed to verify the validity of these conclusions in real road conditions

A “pay-how-you-drive” car insurance approach through cluster analysis

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the “pay-how-you-drive” paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies

Characterizing driving behavior and link to fuel consumption for university campus shuttle minibuses

Abstract: This paper focuses on the effect of aggressive driving behavior on fuel consumption of a vehicle. Different from the traditional statistical analysis method, this paper adopts the frequency domain analysis method to analyze driving aggressiveness and apply a quantitative driving aggressiveness evaluation metric. At the same time, the fuel consumption impact caused by the driving aggressiveness under different driving situations is analyzed. The results are demonstrated for two university shuttle bus. Fuel consumption rate of each vehicle is determined by using available on-board diagnostics (OBD) data including intake air mass flow rate of engine and air/fuel equivalence ratio. The experimental results show that the degree of influence of driving aggressiveness on fuel consumption is not the same in different driving situations. The higher the speed of the driving situation, the greater the difference in fuel consumption caused by driving aggressiveness.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

Battery Aging Studies Based on Real-World Driving

While being a competitive candidate for energy storage systems in automotive applications, lithium-ion battery still needs to overcome fundamental compromises regarding energy density, power density, lifetime, costs and safety concerns. A significant breakthrough can be expected by understanding the real-world customer usage patterns and leveraging this knowledge to develop an optimized battery design and control. However, the challenges of filtering through massive real-world driving data and identifying the features relevant to the real-world battery operations still remain. This dissertation aims to bridge this gap by linking vehicle drive cycles to battery cell duty cycles, which enables quantifying the impacts of real-world variability on battery performance. In addition to performance and efficiency considerations, the methodology enables battery aging analysis in the context of optimal design and control of hybrid electric vehicles. This will facilitate design decisions that ensure adequate performance over the life span of the vehicle with considerations of the battery health objective. The novelty of this work lies in a more accurate method of synthetizing representative real-world drive cycles with a new algorithm to classify road and an innovative quantitative metric of driver style. A modified 48V mild hybrid vehicle model was built to relate the real-world drive cycles all the way to the battery cell duty cycles and to validate the impacts from driver aggressiveness on both the fuel efficiency and the battery loads. The cell duty cycles were further analyzed in frequency domain to synthesize characteristic cell test profiles representative of driver styles and road conditions. A battery cell cycle aging experiment was carried out using the synthesized test profiles. Results validate the positive correlation between driver aggressiveness and cell degradation, and further allow parameter identification of cell electro-chemical model. Modeling effort was extended to generate insights regarding the aging mechanisms, and calibrate a semi-empirical aging model. These tools will enable the inclusion of road conditions and driver styles into the development of battery pack design and propulsion system control hence improving the design assumption fidelity and real-world representativeness of the modeling approach

DRIVER FEEDBACK MOBILE APP

[EN] This paper faces the human factor in driving and its consequences for road safety. It presents the concepts behind the development of a smartphone app capable of evaluating drivers’ performance. The app provides feedback to the driver in terms of a grade (between 0 and 10) depending on the aggressiveness and risks taken while driving. These are computed from the cumulative probability distribution function of the jerks (i.e. the time derivative of acceleration), which are measured using the smartphones’ accelerometer. Different driving contexts (e.g. urban, freeway, congestion, etc.) are identified applying cluster analysis to the measurements, and treated independently. Using regression analysis, the aggressiveness indicator is related to the drivers' safety records and to the probability of having an accident, through the standard DBQ - Driving Behavior Questionnaire. Results from a very limited pilot test show a strong correlation between the 99th percentile of the jerk measurements and the DBQ results. A linear model is fitted. This allows quantifying the safe driving behavior only from smartphone measurements. Finally, this indicator is translated into a normalized grade and feedback to the driver. This feedback will challenge the driver to train and to improve his performance. The phone will be blocked while driving and will incorporate mechanisms to prevent bad practices, like competition in aggressive driving. The app is intended to contribute to the improvement of road safety, one of the major public health problems, by tackling the human factor which is the trigger of the vast majority of traffic accidents. Making explicit and quantifying risky behaviors is the first step towards a safer driving.The author acknowledges the collaboration of JuanPe Roldán and Bernat Garcia for their work on the preliminary data analysis and mathematical contributions, and to Cesar Trapote, for his knowledge and help with the DBQ survey. Thanks also to the 7 drivers who unselfishly participated in the pilot test. Without their contribution this research would not have been possible. This research has been partially funded by the Spanish Ministry of Economy and Competitiveness (TRA2013-45250-R/CARRIL) and partially supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism. Operated by Universidad Complutense de Madrid.Soriguera Marti, F.; Miralles Miquel, E. (2016). DRIVER FEEDBACK MOBILE APP. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1597-1613. https://doi.org/10.4995/CIT2016.2016.4066OCS1597161

Aggressiveness of human melanoma xenograft models is promoted by aneuploidy-driven gene expression deregulation.

Melanoma is a devastating skin cancer characterized by distinct biological subtypes. Besides frequent mutations in growth- and survival-promoting genes like BRAF and NRAS, melanomas additionally harbor complex non-random genomic alterations. Using an integrative approach, we have analysed genomic and gene expression changes in human melanoma cell lines (N=32) derived from primary tumors and various metastatic sites and investigated the relation to local growth aggressiveness as xenografts in immuno-compromised mice (N=22). Although the vast majority >90% of melanoma models harbored mutations in either BRAF or NRAS, significant differences in subcutaneous growth aggressiveness became obvious. Unsupervised clustering revealed that genomic alterations rather than gene expression data reflected this aggressive phenotype, while no association with histology, stage or metastatic site of the original melanoma was found. Genomic clustering allowed separation of melanoma models into two subgroups with differing local growth aggressiveness in vivo. Regarding genes expressed at significantly altered levels between these subgroups, a surprising correlation with the respective gene doses (>85% accordance) was found. Genes deregulated at the DNA and mRNA level included well-known cancer genes partly already linked to melanoma (RAS genes, PTEN, AURKA, MAPK inhibitors Sprouty/Spred), but also novel candidates like SIPA1 (a Rap1GAP). Pathway mining further supported deregulation of Rap1 signaling in the aggressive subgroup e.g. by additional repression of two Rap1GEFs. Accordingly, siRNA-mediated down-regulation of SIPA1 exerted significant effects on clonogenicity, adherence and migration in aggressive melanoma models. Together our data suggest that an aneuploidy-driven gene expression deregulation drives local aggressiveness in human melanoma

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