The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system.

This study investigates a fuzzy controller technique for autonomous robot navigation in both the static and dynamic environmental conditions and an excessive number of pathways to the destination. The design and implementation of a novel obstacle avoidance technique for autonomous robots are developed using the fuzzy controller-based multi-agent system. This method allows the Robot to identify dynamic or static unidentified objects while directing the Robot to prevent collisions and advance toward the objective. The Robot is capable of moving in a variety of environments. The Robot may communicate and travel in dynamic space by perceiving its surroundings and pursuing a free-collision route. This study covers creating a multi-agent system that includes fuzzy logic to regulate the robotic movements along a path reactive for effective Navigation. This project aims to develop an algorithm that allows the Robot to do distinct tasks to accomplish a unified objective, autonomous Navigation in a slightly unfamiliar environment. Under such a situation, the usage of a multi-agent system is advantageous. As a result, we created a framework made up of four agents responsible for sensing, Navigation, dynamic, and static obstacle avoidance. These agents communicate with one another via a coordinating mechanism.

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