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

Cluster Analysis for Driver Aggressiveness Identification

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Neural networks for driver behavior analysis

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering positionbased features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results

Assessing driving risk using Internet of Vehicles data: an analysis based on Generalized Linear Models

With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insuranc

Regression scores to identify risky drivers from braking pulses

[EN] Driving data record information on style and patterns of vehicles that are in motion. These data are analysed to obtain risk scores that can later be implemented in insurance pricing schemes. Scores may also be used in onboard sensors to create risk alerts that help drivers to keep up with safety margins. Regression methods are proposed and a prototype real sample of 253 drivers is analysed. Conclusions are drawn on the mean number of brake pulses per day as measured within 30 seconds time-intervals. Linear and logistic regressions serve to construct a label that classifies drivers. A novel factor based on the driving range that is defined from geo-localization improves the results considerably. Driving range is expressed as measures the diagonal of a rectangle that contains the furthest North-South versus East-West weekly vehicle trajectory. This factor shows that frequent braking activity is negatively related to the square of driving range.Sun, S.; Bi, J.; Guillen, M.; Pérez-Marín, AM. (2020). Regression scores to identify risky drivers from braking pulses. Editorial Universitat Politècnica de València. 59-67. https://doi.org/10.4995/CARMA2020.2020.11514OCS596

A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorith

radiomic features for medical images tamper detection by equivalence checking

Abstract Digital medical images are very easy to be modified for illegal purposes. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. Between the machine that performs medical scans and the radiologist monitor, medical images pass through different devices: in this chain an attacker can perform its malicious action. In this paper we propose a method aimed to avoid medical images modifications by means of equivalence checking. Magnetic images are represented as finite state automata and equivalence checking is exploited to check whether the medical resource have been subject to illegal modifications