Adaptive Hybrid Genetic Algorithm for Autonomous Vehicle Road Network Construction: a Study on GPS Navigation Failure

EasyChair Preprint 3886

Abstract

Study enhances road network construction based on geospatial noisy data reduction. Global positioning system (GPS) applications conforms special standards, formats that establishes an inaccurate model for decision making. A small percentage of major accidents are caused by failures of navigation systems. To construct an established road network an adaptive multi-objective data mining technique is introduced followed by F-score statistical analysis for measuring accuracy of result. Data extracted from GPS are processed using genetic algorithm employing with flexible spatial, temporal, and logical constraint rules, produces an adaptive hybrid genetic algorithm (AHGA). This establishes a framework to construct an optimized road network representing a digital map from real time data. In order to control metaheuristics, data is broken into smaller pieces of improved local search algorithm (Continuous state spaces) in addition towards feature extraction-score concentrates on precision and recall capacities for recognized standards of data extraction, classification, and identification of statistical perception.

Keyphrases: GPS navigation failure, adaptive hybrid genetic algorithm, autonomous vehicle road network construction

@booklet{EasyChair:3886,
  author    = {Md.Nazmus Sakib},
  title     = {Adaptive Hybrid Genetic Algorithm for Autonomous Vehicle Road Network Construction: a Study on GPS Navigation Failure},
  howpublished = {EasyChair Preprint 3886},
  year      = {EasyChair, 2020}}