The Development of Vehicle Position Estimation Algorithms Based on the Use of AMR Sensors

Taghvaeeyan, Saber; Rajamani, Rajesh

doi:10.1109/tits.2012.2208189

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…Magnetoresistive sensor has recently been utilized for vehicle surveillance owing to its advantages of easy installation, low cost, small size, strong anti-jamming capability [4], [5], etc. Scholars worldwide have investigated the application of magnetic sensor and wireless sensor network for vehicle detection and classification.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Classification for Low-Speed Congested Traffic With Anisotropic Magnetoresistive Sensor

Yang

Lei

2015

IEEE Sensors J.

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A vehicle detection and classification system has been developed based on a low-cost triaxial anisotropic magnetoresistive sensor. Considering the characteristics of vehicle magnetic detection signals, especially the signals for low-speed congested traffic in large cities, a novel fixed threshold state machine algorithm based on signal variance is proposed to detect vehicles within a single lane and segment the vehicle signals effectively according to the time information of vehicles entering and leaving the sensor monitoring area. In our experiments, five signal features are extracted, including the signal duration, signal energy, average energy of the signal, ratio of positive and negative energy of x-axis signal, and ratio of positive and negative energy of y-axis signal. Furthermore, the detected vehicles are classified into motorcycles, two-box cars, saloon cars, buses, and Sport Utility Vehicle commercial vehicles based on a classification tree model. The experimental results have shown that the detection accuracy of the proposed algorithm can reach up to 99.05% and the average classification accuracy is 93.66%, which verify the effectiveness of our algorithm for low-speed congested traffic.

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Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Classification for Low-Speed Congested Traffic With Anisotropic Magnetoresistive Sensor

Yang

Lei

2015

IEEE Sensors J.

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“…The Development of Vehicle Position Estimation Algorithms Based on the Use of AMR Sensors [1] [2] This paper focuses on the use of anisotropic magneto resistive (AMR) sensors for imminent crash detection in cars. The AMR sensors are used to measure the magnetic field from another vehicle in close proximity to estimate relative position and velocity from the measurement.…”

Section: Related Workmentioning

confidence: 99%

Two-Dimensional Obstacle Avoidance Using Mobile Robot

Begum¹

2018

IJRASET

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Every year, we find more and more road accidents due to increased traffic on the roads, and if you see the statistics, you will find that the causalities are more every year than that of 1970 Indo -Pak war. Experts say, increased motorist population, long working hours and stressful life are the major reasons for the rise in road accidents. The factors are beyond one's control, but if we could alert the driver on the highway, could save many precious lives. This paper is about advanced technologies in cars for making it more intelligent this work in this papert is to develop an inexpensive sensor system for an automobile that can predict an imminent collision with another vehicle, just before the collision occurs. Here we are connecting sensors to Ardino UNO. If the sensors get activated the controller will give warning sounds by using buzzer. Also it stops the vehicle by using motor.I.

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“…Yimetal [8] proposed a Smart Car Sensor Network (SCSN) to improve the communication reliability for sensor networks. Taghvaeeyan et al [9] proposed position estimation algorithms based on the use of anisotropic magnetoresistive sensors for imminent crash detection in cars. In the work of [10], Taghvaeeyan further proposed a two-dimensional sensor system for automotive crash prediction.…”

Section: Vehicle Sensing Systemsmentioning

confidence: 99%

CGA-based deadlock solving strategies towards vehicle sensing systems

Zheng

Lyu

2014

J Wireless Com Network

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Vehicle sensing system is an important research topic in the research field of Internet-of-Vehicles (IoV). Reliability and real-time performance of vehicle sensing systems are greatly influenced when deadlock happens. When a deadlock is detected, identifying the optimal deadlock solving strategy can ensure that the system goes back to normal state quickly. In order to address this issue, this paper proposes an efficient deadlock solving method. Firstly, the deadlock problem in a vehicle sensing system is analyzed based on four deadlock occurring conditions (i.e., mutual exclusion, hold and wait, no preemption, and circular wait). Secondly, an optimization model is built to combine the quantity and cost of tasks in vehicle sensing systems. After that, a co-evolutionary genetic algorithm (CGA) is developed to search the optimal deadlock solving strategy. Finally, experiments by simulation are conducted and the experimental results show the efficiency of the proposed deadlock solving method for vehicle sensing systems.

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The Development of Vehicle Position Estimation Algorithms Based on the Use of AMR Sensors

Cited by 19 publications

References 10 publications

Vehicle Detection and Classification for Low-Speed Congested Traffic With Anisotropic Magnetoresistive Sensor

Vehicle Detection and Classification for Low-Speed Congested Traffic With Anisotropic Magnetoresistive Sensor

Two-Dimensional Obstacle Avoidance Using Mobile Robot

CGA-based deadlock solving strategies towards vehicle sensing systems

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