Intelligent traffic accident detection system based on mobile edge computing

Liao, Chunxiao; Shou, Guochu; Liu, Yaqiong; Hu, Yihong; Guo, Zhouyi

doi:10.1109/compcomm.2017.8322909

Cited by 24 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on edge computing, some applications for vehicles and traffic management have been proposed. For example, Liao et al [10] proposed an intelligent traffic accident detection system. Tang et al [11] designed the system architecture of a VEC system called PI-edge.…”

Section: Related Workmentioning

confidence: 99%

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

Wang

Zheng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To accommodate the exponentially increasing computation demands of vehicle-based applications, vehicular edge computing (VEC) system was introduced. This paper considers a three-layer VEC architecture and proposes an online offloading scheduling and resource allocation (OOSRA) algorithm to improve the system performance. Specifically, this study designs a game-theoretic online algorithm to solve the problem of computation task offloading scheduling, and employs an online bin-packing algorithm to compute the resource allocation modified from the First Fit algorithm, which can be adapted to various traffic flow and service attributes. Extensive simulations are conducted, and a numerical analysis of simulation results verifies the effectiveness of the OOSRA-VEC system. The algorithms proposed in this paper are online, adaptive, and distributed, which can provide useful references for future development in VEC system protocols. INDEX TERMS Intelligent vehicles, intelligent transportation systems, edge-computing, game theory.

show abstract

Section: Related Workmentioning

confidence: 99%

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

Wang

Zheng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The methods of accident detection using smartphones were developed in [17]- [22]. In [17] data from an accelerometer was combined with gyroscopic information and sound samples from microphone.…”

Section: State Of the Art In Accident Detectionmentioning

confidence: 99%

“…The main disadvantages of these approaches are significant increase of the overall processing time and noticeable level of false positive and false negative results. An intelligent system which uses mobile edge computing to detect traffic accidents using a smartphone was proposed in [22]. The acceleration and speed of vehicle are analysed and images showing accident scenes are identified.…”

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

“…The authors formulate a multi-user multi-tasking computing download problem for the green MEC, and use the Lyanponuv optimization approach to determine the energy collection policy. In [Liao et al 2017] the authors propose an intelligent MEC-based traffic accident detection system with proximity, low latency and vehicle identification, requiring the provision of computational resources for real-time responses.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Resource Allocation in Hierarchically Distributed Data Centers

Vieira¹,

Teixeira²,

Cardoso³

2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

View full text Add to dashboard Cite

The current networks infrastructure needs to support the rapidly increasing data traffic. Sophisticated planning approaches must be adopted by the operators so the high number of applications can be managed efficiently. In this work, a resource provisioning model for hierarchically distributed data centers is proposed using Integer Linear Programming (ILP). The objective is to increase the efficiency in the use of computational resources and decrease the overhead in network links. Results show that the model is able to efficiently accommodate 20% more applications when compared to the First-Fit approach.

show abstract

Intelligent traffic accident detection system based on mobile edge computing

Cited by 24 publications

References 9 publications

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

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

Optimizing Resource Allocation in Hierarchically Distributed Data Centers

Contact Info

Product

Resources

About