پروژه های آماده اینترنت اشیاء

این پروژه یک مقاله آماده با موضوع، یک مرور کلی از روش‌ها و الگوریتم‌های پیشگیری از تصادفات وسایل نقلیه خودران در ۲۲ صفحه ورد می باشد. در ادامه چکیده ای از این مقاله را خواهیم داشت: چکیده: An overview of the methods and algorithms of collision prevention of self-driving vehicles Abstract The term Internet of Vehicles (IoV) has emerged to denote an interconnected collection of vehicles that provide information for common services, such as traffic management and traffic safety. Due to the increase in vehicle accidents, there is a growing need for high - performance vehicle crash detection systems. In Internet of Things (IoT) and IoV networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. The use of reinforcement learning (RL) for dynamic obstacle avoidance algorithms and path planning has become increasingly popular in recent years. With the advancement of technology, vehicles are becoming more and more connected, and in the future, vehicles and their infrastructure can be remotely connected using cellular systems. Vehicle communication is a key factor for many automotive-related applications. It is possible to consolidate safety, maintenance, forecasting and information gathering operations using non-collision modeling. In this research, based on the concepts of the IoT, the advantages of the convergence of common technologies and the solutions of this field and the management of non-collision are to be tested again and the necessary optimization is applied. The main problem is how to implement in a non-collision based on the IoT. Therefore, the authors of this research propose a vehicle crash detection system that uses deep learning techniques Deep Q-Learning (DDQN) to rapidly evaluate risks associated with all surrounding vehicles and to maneuver the vehicle into a safer region when faced with critically dangerous situations are used to develop a vehicle crash detection system. In the proposed method of a risk assessment module based on factors of location, speed and acceleration, the safest route is defined by reducing the level of risk among the existing routes, the results of which should lead to a reduction in collisions of self-driving vehicles in the defined routes.