Fuzzy-Based Clustering for Larger-Scale Deep Learning in Autonomous Systems Based on Fusion Data

Abstract

Problems in autonomous systems may be tackled with the help of the AS-FC-DL approach, which integrates autonomous fuzzy clustering and deep learning methods. The system can anticipate human behavior on crowded roadways by employing these techniques to recognize patterns and extract features from complicated unsupervised data. Each image point's membership value is associated with the cluster's epicenter using the fuzzy clustering methodology in the AS-FC-DL approach. Using least-squares methods, this approach finds the optimal position for each data point within a probability space, which may be anywhere among multiple clusters. Data points from an unlabeled dataset may be organized into distinct groups using a deep learning technique called cluster analysis. Data fusion from many sources, including sensor data and video data, can improve the AS-FC-DL method's precision and performance. The algorithm is able to deliver an all-encompassing and precise evaluation of human behavior on crowded roadways by fusing data from many sources. The AS-FC-DL approach may also be employed in autonomous vehicles to help them learn from their experiences and improve their performance. Using reinforcement learning, a model for autonomous vehicle driving may be constructed. The AS-FC-DL approach helps the self-driving car traverse the area with increased precision and efficiency by allowing it to recognize structures and extract features from complicated unsupervised data.