Telematics System in Usage Based Motor Insurance

Husnjak, Siniša; Peraković, Dragan; Forenbacher, Ivan; Mumdziev, Marijan

doi:10.1016/j.proeng.2015.01.436

Cited by 100 publications

(58 citation statements)

References 4 publications

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“…Automated licence plate recognition is already commonplace, and vehicles are increasingly equipped with event data recorders and dashboard cameras. Similarly, the idea of use-based insurance (pay-how-you-drive business models) is already applied by insurance companies worldwide [60], whereas live traffic information is available from various telematics applications (e.g., [27]). Considering that Facebook's DeepFace has been used to identify people from pictures with high accuracy [61], it should also be possible to classify road users using facial recognition, provided that camera resolution is sufficient.…”

Section: Discussionmentioning

confidence: 99%

Will vehicle data be shared to address the how, where, and who of traffic accidents?

et al. 2019

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Vehicles are increasingly equipped with sensors that measure the state of the vehicle and surrounding road users. Although most of these sensor data currently remain local to the vehicle, the data could be shared with the aim to improve road safety. We postulate that there is a range of scenarios regarding data sharing, with two extremes: In scenario 1, the acquired shared data will be analysed regarding the how, where, and who of road traffic errors, violations, and accidents; actions can then be taken to improve automated driving systems, manage accident hotspots, and provide personalised feedback, rewards, or penalties to road users. In scenario 2, the recorded data will not be shared, because of privacy concerns. We conclude that there exists a tension between a position of utilitarian use of data and a position of privacy.

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Section: Discussionmentioning

confidence: 99%

Will vehicle data be shared to address the how, where, and who of traffic accidents?

et al. 2019

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“…Usage‐based insurance (UBI) is a telematics‐based alternative whereby dynamic risk factors, based on behavioral characteristics and actual driving use or travel exposure, are employed. The two most common are Pay‐As‐You‐Drive (PAYD) and Pay‐How‐You‐Drive (PHYD) (Husnjak, Peraković, Forenbacher, & Mumdziev, ), which monitor usage patterns and driving behavior, respectively. Telematics‐based insurance plans allow insurers to dynamically reassess drivers based on behavior and travel patterns.…”

Section: Emerging Technology Risksmentioning

confidence: 99%

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

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“…The increase in the usage results in a greater number of applications and their instances, and thus a larger number of the existing devices and the transmitted data [29]. The end result is an increase of the potential attacks surface proportionally with the use of the IoT concept within the observed sector.…”

Section: Security Aspect Of the Application Layermentioning

confidence: 99%

Classification of Security Risks in the IoT Environment

Cvitić¹,

Vujić²,

Husnjak³

2016

DAAAM Proceedings

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The concept of Internet of Things (IoT) is based on a layered architecture. Each of the layers includes the application of a range of diverse technologies for the data transmission, processing and storage. This paper will explore the vulnerabilities and threats in IoT environment and protection methods that can be implemented within such an environment due to the hardware limitations of the existing equipment and technology used for data transfer. Based on the results of the research, classification of security risks of the architectures' particular layer, as well as security risks depending on the type of use of IoT concept will be proposed. The classification of risk will provide the opportunity to direct further research on the most vulnerable layers of the architecture and implementation of appropriate methods of protection, depending on the application of this concept. This research was conducted in order to provide accurate information for visually impaired people.

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Telematics System in Usage Based Motor Insurance

Cited by 100 publications

References 4 publications

Will vehicle data be shared to address the how, where, and who of traffic accidents?

Will vehicle data be shared to address the how, where, and who of traffic accidents?

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

Classification of Security Risks in the IoT Environment

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