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In today's scenario, new automobiles are fitted with various innovative safety systems. Safety systems, restraints and all the vital submissive protection elements are standard features. Now automobiles are commonly fitted with more sophisticated effective protection technologies that may avoid collisions. The functionalities of the Advanced Driver Assistance Systems are continually expanding. A survey of the highest common available technologies utilized in ADAS and explanations of their possible applications are addressed in this work. [ABSTRACT FROM AUTHOR]

In modern era, road traffic density has been increasing requiring a smart traffic system in managing traffic. Traffic congestion management has been a challenge nowadays as there is sharp increase in the number of vehicles. Real-time vehicle monitoring techniques play an important part in prevention of recurring traffic jams, managing traffic on roads, and deadly accidents. The most notable challenges are operating the system in real time to accurately identify and classify vehicles in active traffic flows without obstructing the vehicle. Factors such as weather environment and video quality can become objectives in vehicle detection using traditional machine learning algorithms leading to poor detection results. Recently, for better application in traffic control systems for vehicles, there has been a move towards deep learning architectures. In this paper, YOLOv2 (You Only Look Once) principle is utilized for real-time detection and counting of vehicles. The working of proposed strategy is carried out in two phases where vehicle detection is done at first phase and then counting of moving vehicles during second phase. This method provides strong object positioning functionality and high frames per second (FPS). The test results confirm that higher detection accuracy can be achieved with the proposed method, even when detecting small vehicles. This mechanism of YOLO based system provided an average detection of 95.75% and counting accuracy of 96.85%. The implications of these results help in intelligent transport planning. [ABSTRACT FROM AUTHOR]

The demand for advanced driver assistance systems (ADAS) based on mutual management has grown in the recent decade, with automation continually assisting the driver at the circuit level with flexible authority. A quick review of the literature indicates a continual endeavour to improve theoretical understanding of shared control, and a wide range of vehicle application areas with a growing series of works in recent days. However, there is no worldwide synthesis of these activities. For that purpose, this paper examines the whole subject of shared control in autonomous cars, focusing on the following elements: such as an idea, classifications, procedures, and technological state. Theoretical and implementation contributions are separated in the research. There is a clear difference between linked and detached shared control based on them. Additionally, prototype and model-free techniques from these two groups are assessed independently, with an emphasis on systems that use the steering column as the control interface. To examine the performance of such systems, prototype actuators are evaluated by at least one actual driver. The incorporation of a vehicle driver helps in decreasing disagreements at the steering wheel, according to the findings. Factors such as driver condition, driving concentration, and safety signs also have a significant influence on competence computation. In terms of testing, driver-in-the-loop simulators are the most prevalent venues, with just a few experiments conducted in actual automobiles. In the future years, it is projected to be implemented in prototype automobiles. [ABSTRACT FROM AUTHOR]

Within regulated conditions, autonomous systems have improved their skills for substantially and even completely automated operation. Embedded detectors and telecommunication wireless routers, such as hardware and other automobiles, are used to gather visual data. Multiple moving management and forecasting approaches have been devised to independently drive in complicated surroundings looking at the data. The primary purpose is to implement techniques that promote security, convenience, and power efficiency. Nevertheless, challenging issues including such steering in urban complex situations with inertial guidance functionality, i.e., susceptible motorists and automobiles, and collaborative manoeuvres between computer controlled and semi-automated automobiles still require more work before even being implemented in a real-world situation. This study provides an overview of mobility planning strategies used in the research on automated cars. It also provides a definition of the method adopted by research organizations, their inputs to trajectory planning, and a contrast of these methods. Relevant work in the areas of approaching and obstacle detection manoeuvres is given, providing for a deeper understanding of the limitations and issues that will need to be solved in the coming years. Furthermore, there is a summary of suggestions for future research and uses. [ABSTRACT FROM AUTHOR]