Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.

This paper focus on the simulation and hardware analysis of a diode clamped multilevel inverter (DCMLI) fed direct torque control (DTC) permanent magnet synchronous motor (PMSM) drive in electric vehicle (EV) application. DTC-PMSM drive is more used for torque and speed control. The existing DTC PMSM drive consists of torque and flux hysteresis comparators and suffers from variable switching frequency and torque ripples. These problems can be solved by using carrier-based space vector modulation (CBSVM) about torque and flux. In this proposed approach a DCMLI fed 4 poles, 0.5 HP DTC PMSM drive system is designed and simulated using carrier-based CBSVM. Simulation and experimental implementation are carried out in MATLAB environment and AVR Microcontroller respectively. The simulated performances are studied in steady-state and transient conditions for varying load, speed, and torque. The results of the DTC-PMSM drive system using CBSVM show that the proposed method can effectively reduce the torque ripple and maintain a constant speed and also improved driving performance of drive for electric vehicle applications.