Autonomous cars have achieved exceptional growth in the automotive industry in the last century in terms of reliability, safety and affordability. Due to significant advancements in computing, communication and other technologies, today we are in the era of autonomous cars. A number of prototype models of autonomous cars have been tested covering several miles of test drives. Many prominent car manufacturers have started investing huge resources in this technology to make it commercialize in the near future years. But to achieve this goal still there are a number of technical and non-technical challenges that exist in terms of real-time implementation, consumer satisfaction, security and privacy concerns, policies and regulations. In summary, this survey paper presents a comprehensive and up-to-date overview of the latest developments in the field of autonomous cars, including cutting-edge technologies, innovative applications, and testing. It addresses the key obstacles and challenges hindering the progress of autonomous car development, making it a valuable resource for anyone interested in understanding the current state of the art and future potential of autonomous cars.

In the last couple of decades, number of vehicles has been increased drastically. Hence it has become very difficult to keep track of each and every vehicle for the purpose of traffic management and the law enforcement. Use of Automation number Plate Recognition is increasingly now days for maintaining traffic activities and as similar to the method of automatic electronic toll collection. In the past, from the survey many techniques and algorithms have been proposed for number plate detection and recognition, each technique having its own advantages and disadvantages. The fundamental step in number plate detection is localization of a number plate. The proposed Automatic Number Plate Recognition (ANPR) System is based on an image processing technology. The proposed system can be mainly used to monitor road traffic activities such as the identification of vehicle during traffic violations such as speed of vehicle and to detect at the street traffic signals lane violation. And thereby can be traced every vehicle for traffic rule violation and can provide the information to the concern authority to take further effective action, so we can have smooth traffic flow and also we can avoid accidents occurring on the traffic junction. This system can also be used to assist the authorities in identifying for any stolen vehicle. The proposed system first detects for any vehicle which violates traffic rule and then captures the vehicle image. From the captured image using image segmentation technique the vehicle number plate region will be extracted. And the technique used for the character recognition on number plate is Optical character recognition. The system is implemented and simulated using Matlab. Next we have feedback system i.e., the vehicle number which is extracted using the proposed ANPR algorithm from PC which is then given to GSM modem for further SMS feedback system to the user and concern authority. The system design also involves the design and development of GUI using Matlab, to ease the user in step by step recognizing the characters and numbers from the vehicle license plate and displaying on the desktop GUI screen.

Multi activity-multi object recognition (MAMO) is a challenging task in visual systems for monitoring, recognizing and alerting in various public places, such as universities, hospitals and airports. While both academic and commercial researchers are aiming towards automatic tracking of human activities in intelligent video surveillance using deep learning frameworks. This is required for many real time applications to detect unusual/suspicious activities like tracking of suspicious behaviour in crime events etc. The primary purpose of this paper is to render a multi class activity prediction in individuals as well as groups from video sequences by using the state-of-the-art object detector You Look only Once (YOLOv3). By optimum utilization of the geographical information of cameras and YOLO object detection framework, a Deep Landmark model recognize a simple to complex human actions on gray scale to RGB image frames of video sequences. This model is tested and compared with various benchmark datasets and found to be the most precise model for detecting human activities in video streams. Upon analysing the experimental results, it has been observed that the proposed method shows superior performance as well as high accuracy.

Medicines are the primitive solution for the prevention and cure for most of the diseases. Many risky diseases can be cured and prevented with the use of proper medication. The main objective of our proposed system, is to ensure a user friendly design that the patients can use as a reminder alert to take their daily medications on time. A wearable device is designed which is used to monitor the heartrate. When there are unambiguous changes in the heartrate, the user is alerted by a vibration to take the necessary drug. Also an Email is sent, reminding the user about the same.