Automatic crash detection system for two-wheeled vehicles: design and experimental validation

Gelmini, Simone; Strada, Silvia; Tanelli, Mara; Savaresi, Sergio M.; Tommasi, Claudio De

doi:10.1016/j.ifacol.2019.09.079

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…[18] examined the use of telematic devices in semi-autonomous vehicles and discussed how this technology will require great collaboration between the car manufacturers and insurance companies in order to get a full understanding of the risks. Some authors raise another question for telematic devices and their use in the insurance context [19]. Namely, these authors emphasize the difference for telematics of two-wheelers and provide a novel crash detection algorithm proved against the experimental data for this type of vehicles.…”

Section: The Dissection Of Relationships Among Main Stakeholdersmentioning

confidence: 99%

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An Acceptance Approach for Novel Technologies in Car Insurance

et al. 2020

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Background: Unlike other financial services, technology-driven changes in the insurance industry have not been a vastly explored topic in scholarly literature. Incumbent insurance companies have hitherto been holding their positions using the complexity of the product, heavy regulation, and gigantic balance sheets as paramount factors for a relatively slow digitalization and technological transformation. However, new technologies such as car telematic devices have been creating a new insurance ecosystem. The aim of this study is to assess the telematics technology acceptance for insurance purposes. Methods: The study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). By interviewing 502 new car buyers, we tested the factors that affect the potential usage of telematic devices for insurance purposes. Results: The results indicate that facilitating conditions are the main predictor of telematics use. Moreover, privacy concerns related to the potential abuse of driving behavior data play an important role in technology acceptance. Conclusions: Although novel insurance technologies are mainly presented as user-driven, users (drivers and insurance buyers) are often neglected as an active party in the development of such technologies.

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Section: The Dissection Of Relationships Among Main Stakeholdersmentioning

confidence: 99%

“…Ultimately, risk premiums will decrease. As stated by [6] (p. 19), "[b]y recording data on drivers' behavior, the information asymmetry between the policyholder and the insurer is reduced, enabling a granular risk differentiation based on the true risk levels of drivers. "…”

Section: Introductionmentioning

confidence: 99%

An Acceptance Approach for Novel Technologies in Car Insurance

et al. 2020

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“…For this reason, in the experimental campaign we equipped all tested vehicles with the same external device, which was a modern, cost-effective, and easy to install telematic e-Box [17]. This apparatus was already employed in different applications [18], [19], [20], proving to be a flexible and reliable device for similar applications. E-Boxes are equipped with an inertial measurement unit (IMU) and a GPS/GNSS receiver.…”

Section: Problem Statement and Experimental Setupmentioning

confidence: 99%

Vehicle Vertical Wearing Index (V² WI): active monitoring of wearing and aging of vertical-dynamics components in four-wheeled vehicles

Gelmini

Centurioni

Pivaro

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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Being able to assess the state-of-health of a vehicle opens of course many possible applications. All the more so if the ongoing degradation of the monitored components can be provided continuously as the vehicle life extends over time. In modern shared mobility systems, thanks to which migration from ownership to usership models should eventually take place, developing means to actively monitor the state of the vehicle fleet is crucial to improve business models and feasible and predictive maintenance plans. Within this challenging context, the present paper focuses on the monitoring of the vehicle vertical dynamics, to understand, from the analysis of measured data, which is the combined effect of drivingstyle and introduce road pavement roughness in determining the usage profile of the vertical-dynamics-related components of the vehicle, mostly the suspensions system. The proposed cost function concisely represents such wearing process, with the advantage of not requiring detailed parametric models of the vehicle dynamics and of the components themselves. The approach is tested on more than 9.000 km of trips carried out on four different vehicles, allowing to prove the effectiveness and generality of the approach.

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“…In this situation, the detection and classification of abnormal events present in the video frame sequence is also highly difficult [13][14][15]. Deep learning models are used for detection and classification process of abnormal event detection, since it plays an anchor role in the development of an automated abnormality detector that can handle the information associated with millions of video frames [16][17][18][19].Further, the process of video summarization becomes necessary for the principle of facilitating the information storage, simplifying the investigation of data and enhancing the access rate related to each individual video frame for obtaining maximized event frame content [20][21][22][23]. The video sequences permits the selection of key frames with abnormal events based on feature extraction and appropriate clustering schemes [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

An integrated framework for abnormal event detection and video summarization using deep learning

2022

IJATEE

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This field of research is considered to be essential for the enhancement of video surveillance systems that necessitates vast space of storage and analysis of complex data.Abnormal event detection, classification and video summarization is considered to be an important process that results in handling the challenges for improvement of video surveillance systems [5,6].The main issues in abnormal event detection and summarization is of immense concern to the research community in intelligent transportation systems [7,8]. It is very difficult from the state of the art techniques to provide the accurate detection and classification of abnormal events in the video frames andto summarize the key events. The core aim of the work is to develop the higher accuracy in detection, class ification and Re search Article

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Automatic crash detection system for two-wheeled vehicles: design and experimental validation

Cited by 11 publications

References 10 publications

An Acceptance Approach for Novel Technologies in Car Insurance

An Acceptance Approach for Novel Technologies in Car Insurance

Vehicle Vertical Wearing Index (V² WI): active monitoring of wearing and aging of vertical-dynamics components in four-wheeled vehicles

An integrated framework for abnormal event detection and video summarization using deep learning

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Automatic crash detection system for two-wheeled vehicles: design and experimental validation

Cited by 11 publications

References 10 publications

An Acceptance Approach for Novel Technologies in Car Insurance

An Acceptance Approach for Novel Technologies in Car Insurance

Vehicle Vertical Wearing Index (V2 WI): active monitoring of wearing and aging of vertical-dynamics components in four-wheeled vehicles

An integrated framework for abnormal event detection and video summarization using deep learning

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Product

Resources

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Vehicle Vertical Wearing Index (V² WI): active monitoring of wearing and aging of vertical-dynamics components in four-wheeled vehicles