Collision Detection Algorithms in the Ecall System

Kamiński, T.; Niezgoda, M.; Kruszewski, Mikołaj; Grzeszczyk, R.; Drop, T.; Filipek, P.

doi:10.5604/12314005.1138356

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such algorithms are used in newer cars for airbags and air curtains systems, however, they cannot be directly used for eCall system. It is because they are programmed to be used before accident [24] (to prevent passengers from being hurt) but eCall demands information after the accident. The objective of the proposed compact eCall-compliant device is to be used after accident.…”

Section: State Of the Art In Accident Detectionmentioning

confidence: 99%

A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

et al. 2020

View full text Add to dashboard Cite

Safety is one of the central elements of society functioning. The eSafety aims at increasing road safety through the use of intelligent integrated safety systems. This article presents the concept and PCB implementation of an on-board compact eCall device that can be installed at the owners' request in a used vehicle. The prototype includes accident and rollover detection, passenger detection, and GSM communication with the eCall PSAP service center. Two accident detection algorithms are implemented: the first relates to the collision detection based on exceeding the threshold of acceleration using an accelerometer to measure the dynamic force caused by movement and the gravity force, the second-the rollover detection based on exceeding the threshold of the vehicle angle of inclination using data coming from the accelerometer and a gyroscope. The prototype was tested in eight different test scenarios using laboratory accident simulators as well as during driving. The proposed solution of passenger detection is based on images coming from a built-in 360 • view camera. In the authors' approach the obtained images were transformed into linear ones and subject to subsequent operations-initial processing, object detection and redundant detection filtering. The authors utilize biometric methods in object detection such as Viola-Jones and Single Shot MultiBox Detector for face localization, and You Look Only Once for whole body detection. An additional testing mechanism for passenger detection are seat pressure pads that are independent of camera detection. All necessary sensors and communication components verified by practical experiments have been integrated on the custom designed motherboard with the NVIDIA Jetson Nano processing module installed. The tests confirmed the performance is sufficient for rapid accident detection, real-time image analysis and all mandatory eCall communication functions. Results of experiments were promising, providing high accident and passenger detection rates.

show abstract

Section: State Of the Art In Accident Detectionmentioning

confidence: 99%

A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Accident detection technologies are the technologies to trigger and activate Supplemental Restraint System (SRS) like airbags and seatbelts used in different vehicles [11]. This paper proposed to use acceleration and curvature of the moving vehicle as two significant parameters which are obtained by calculation of accelerometers and gyroscopes measurement.…”

Section: Accuracy Requirement Of Location Based Services Mostly Depenmentioning

confidence: 99%

Collision Vehicle Detection System Based on GPS/INS Integration

Navidi¹,

Landry²

2017

JCC

View full text Add to dashboard Cite

Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.

show abstract