Car Crash: Sensing, Monitoring and Detection
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8097
Special Issue Editors
Interests: dynamics; vibrations; damping; fluid-structure & electro; mechanic interaction; vehicle dynamics
Interests: SHM; EMD; HHT; FBG; irreversible phenomena
Special Issue Information
Dear Colleagues,
Sensors (ISSN 1424-8220; CODEN: SENSC9) is the leading international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) and Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Sensors, and their members receive a discount on the article processing charges.https://www.mdpi.com/journal/sensors
Car motion monitoring by special sensors is a technology attracting a large number of researchers and companies all over the world. Many strategies of sensing are under investigation, ranging from accelerometer sensors to image processing and acoustic emission in the case of crashes, involving data fusion processes. Sensors, sampling circuits and digital techniques for data reduction and algorithms for signal analysis and data processing are challenging, especially for good classification performance and acceptable computational costs. Many uses of the information captured by the onboard sensors are possible. Safety alerts sent by the car in the case of an accident permits the real-time identification of injured persons and immediate rescue. The chance of making a classification and distinction between real accident events and possible frauds has important implications for insurance companies. Finally, the monitoring of driving behaviour is key for attributing a safety score to personal driving styles and the possible associated insurance costs.
The topics for this issue include, but are not limited to, the following:
- Sensors and techniques for car crash monitoring and driving behaviour;
- Sensor data fusion and integration;
- Sensor data reduction through digital signal processing techniques and adaptive firmware;
- The integration of sensor data by modelling and simulation;
- Classification techniques for car crashes, by signal analysis, machine learning and AI tools;
- Experimental campaigns for crash data acquisition.
Prof. Dr. Antonio Carcatera
Dr. Nicola Roveri
Dr. Gianluca Pepe
Guest Editors
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