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International Journal of Engineering and Advanced Research Technology

Volume 3 Issue 2 (February 2017)

S.No. Title & Authors Page No View
1

Title : Z-SDLC Model: A New Model For Software Development Life Cycle (SDLC)

Authors : Syed Zaffar Iqbal, Muhammad Idrees

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Abstract :

Software Development Life Cycle (SDLC) Models are the frameworks used to design, develop and test the software project. The SDLC models are set of procedures which are to be followed during the software development process. These SDLC models make sure that the software development is according to the needs of the client/customer and insure software will design within the given timeframe and budget. There are many SDLC models used during software development process. These models are also referred as Software Development Process Models (SDPM). Each process model follows a sequence of steps, in order to ensure success in process of software development. We have different types of SDLC models. The SDLC models are waterfall model, iterative model, spiral model, V-model, agile model, RAD model and prototype model. Each of these models has its own weaknesses and strengths. In this paper I develop a new model called Z-SDLC model for software development that lays special emphasis on client/customer satisfaction and also tries to fulfil the objective of the Software Engineering for the development of high quality software product within timeframe/schedule and budget. The new proposed model is designed in such a way that it allows client/customer and software company to interact freely with each other in order to understand and implement requirements in a healthier way.

1-8
2

Title : Feature Extraction of Pedestrian Behavior Propensity based on BP Neural Network

Authors : Zhenxue Liu, Yaqi Liu, Haibo Wang, Wei Tian, Xiaoyuan Wang

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Abstract :

Pedestrian is an important part of the traffic system, pedestrian travel safety, efficiency, comfort and so on have received more and more people's attention. Under the premise of the internet of pedestrians, it is of great significance to timely implement pedestrian safety warning for improving its active safety. The prerequisite for the implementation of the safety warning is to accurately identify the pedestrian's intention, and the pedestrian's intention is affected by many factors, among which the difference of the individual characteristics of the pedestrian is an important factor causing the difference of the movement intention. Thus, the reasonable pedestrian classification is of great significance to construct the scientific and reasonable pedestrian safety early warning system. Aimed at the differences of pedestrian traffic microcosmic behavior, the individual characteristics of pedestrians influencing pedestrian movement intention were analyzed thoroughly. Limited to the availability of psychological parameters of pedestrian individual differences and the external influencing factors, the concept of pedestrian behavior propensity was put forward learning from the concept of driving propensity, and it was used to describe the differences of the individual characteristics. Pedestrians were divided into three types of safety, task-based and comfortable according to the difference of actual pedestrian traffic behavior. Combined with the questionnaire survey, the non-invasive natural walking observation experiment was used to collect the movement data of three types of pedestrians. The pedestrian behavior in free flow was taken as an example, the feature vector of pedestrian behavior propensity was analyzed and extracted based on BP neural network. This study can provide theoretical support for the construction of scientific and reasonable pedestrian classification model and personalized pedestrian safety warning system.

9-14
3

Title : Simulation Model of Driving Behavior Based on Nonparametric Kernel Density Estimate

Authors : Yaqi Liu, Zhenxue Liu, Wei Tian, Haibo Wang, Xiaoyuan Wang

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Abstract :

The research on car-following behavior is an important part of microscopic research in traffic flow theory. With the continuous development of Intelligent Transportation System (ITS) and the deepening of big data research, the car-following simulation model based on a series of assumptions and the single control rule has been difficult to meet the precision requirement. In order to overcome the shortcomings of previous car-following models, a real vehicle driving test on a city road was designed and a series of data acquisition equipment, mainly including two experiment vehicles, speedometer and range sensor, were used to collect a variety of driving behavior data. The information mining technology was used to extract the valuable driving behavior information from measured data in this paper. The noise was eliminated by the Nonparametric Kernel Density Estimate (NKDE), and a new simulation model of driving behavior was established based on nonparametric regression. A simulation experiment was designed to verify the validity of the car-following model. The main contents of model validation included acceleration, velocity, relative velocity and relative distance. The measured running state was compared with the simulated result, and the comparison between the measured value and the simulated value can fit well. The simulation results showed that the model can effectively reflect the driving behavior in the car-following process. The research results of this paper can provide new ideas and methods for the study of microscopic traffic flow in the era of big data. The research results are of great significance to improve traffic safety.

15-19
4

Title : Vehicle target clustering identification algorithm based on 3D Lidar point cloud

Authors : KONG-Dong, QU Yun-peng, KONG Peng-fei, Gao Hong-chen

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Abstract :

A new vehicle target clustering identification algorithm is proposed based on the characteristics of the elevation of the structured road area and the road boundary 3D point cloud collected by the 32-line laser radar and the projection characteristics of the three-dimensional point cloud data of the vehicle target in the structured road environment. Firstly, the algorithm divides the area of interest of the smart car into six regions based on the origin of the 32-line laser radar coordinate system: right front, right rear, right, left rear, left front, left side, and the road boundary is identified based on the established structured road model, thereby reducing the interference of the obstacle outside the road boundary to the identification of the vehicle target and improving the real-time performance of the data processing. Secondly, based on the characteristics of the point cloud data of the target surface of the laser radar and the shape projection characteristics of the vehicle target, the clustering algorithm of the distance threshold is adjusted by the adaptive region. The distance threshold can be automatically based on the different regions of interest adjustment. Finally, the vehicle target is accurately identified by extracting the internal feature points of the clustering target and calculating the angle of the feature point vector or the length of the module. Experimental results show that the proposed algorithm can accurately identify the vehicle target in the structured road area, and the accuracy and robustness of the vehicle can meet the requirements of the vehicle.

20-23