Inertial Information Fusion for Improved Vehicular Perception Systems

Durdona Uktamova

Department of International Business Management, Tashkent State University of Economics, Uzbekistan

Abstract

Advancing the capabilities of vehicle perception systems, through the fusion of sensor data is a pursuit in the field of vehicles and intelligent transportation systems. This study explores the complexities involved in enhancing vehicle perception with the goal of tackling the challenges associated with interpreting various sensor inputs to gain an understanding of the environment. By utilizing techniques that fuse information and clustering methodologies this research aims to identify driving scenarios based on patterns observed in sensor data allowing for a nuanced analysis of environmental variations. Additionally a classification framework using Convolutional Neural Networks (CNNs) is employed to accurately classify types of road surfaces demonstrating how deep learning models can effectively utilize sensor representations for environmental characterization. The methods employed encompass clustered data fusion, where K means clustering is utilized to segment sensor data into scenarios and CNN classification, for accurate identification of road surface types. The study achieved impressive findings using these methodologies, exhibiting unique clusters typical of various driving circumstances based on sensor aggregation and demonstrating the CNN's capacity for accurate road surface classification.

Keywords: Information Fusion; Inertial Data; Vehicular Perception; Machine Learning.