In-Car eCall Device for Automatic Accident Detection, Passengers Counting and Alarming

Lupinska-Dubicka, Anna; Tabędzki, Marek; Adamski, Marcin; Rybnik, Mariusz; Omieljanowicz, Miroslaw; Szymkowski, Maciej; Gruszewski, Marek; Klimowicz, Adam; Rubin, Grzegorz; Saeed, Khalid

doi:10.1007/978-3-662-61092-3_3

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…The usage of smartphones and GPS-based applications are also popular for their good accuracy and real-time performance, but smart phones have privacy complications, and due to false alarm filter, it may not detect all accidents [29]. In-vehicle emerging technologies appearing in, for example, eCall , Onstar, and Ford-Sync [30], [31] can automatically make an emergency call after an accident. These services require cross-border interoperability, because vehicles cross national borders and because of the global and regional nature of the automotive market.…”

Section: Related Workmentioning

confidence: 99%

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

Mehrannia¹,

Bagi²,

Moshiri³

et al. 2021

Preprint

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Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using Long-Short Term Memory (LSTM) network for automatic detection of freeway accidents. The LSTM-based framework increases class separability in the encoded feature space while reducing the dimension of data. Our experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 minutes with a true positive rate of 0.71 and a false positive rate of 0.25 which outperforms other competing methods in the same arrangement.

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Section: Related Workmentioning

confidence: 99%

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

Mehrannia¹,

Bagi²,

Moshiri³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The advanced accident simulator was shown in Figure 2. The main parts of the second laboratory set are: a steel frame (13) on blocked wheels (14), rubber expanders (3), a sliding mechanism (4), a trigger (2) that releases a pusher element (1), a trolley (5) with rotating platform (6) and a crash wall (7). All potentially dangerous parts of system are protected by covers (15) during a test.…”

Section: B Advanced Accident Simulatormentioning

confidence: 99%

“…Next, the trolley hits the wall with impact force which depends on number of used expanders. The advanced impact simulator was more precisely described in work [7].…”

Section: B Advanced Accident Simulatormentioning

confidence: 99%

“…As described in [7] very important part of data sent to the eCall center is number of passengers inside the vehicle during the collision. This subsection contains the literature review of the algorithms connected with human presence detection and our idea how to solve this problem.…”

Section: State Of the Art In Human Presence Detectionmentioning

confidence: 99%

“…This work is based on the authors' previous publications [4] [5] [6] [7]. In this article those particular works and approaches are generalized and extended with further details, regarding among others: 1) details on experimental accident simulation and accident data gathering using mounted eCall prototype devices and two accident simulators, created by the authors, 2) extended state of the art for human presence detection (in a vehicle) and details on selected approaches as well as used hardware and excerpt of detection results obtained.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

Self Cite

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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.

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A review on cooperative perception and control supported infrastructure-vehicle system

Yu¹,

Li²,

Wang³

et al. 2022

Green Energy and Intelligent Transportation

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In-Car eCall Device for Automatic Accident Detection, Passengers Counting and Alarming

Cited by 5 publications

References 12 publications

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection

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

A review on cooperative perception and control supported infrastructure-vehicle system

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