{"id":205,"date":"2023-07-14T11:37:22","date_gmt":"2023-07-14T11:37:22","guid":{"rendered":"https:\/\/34.239.202.173\/?p=205"},"modified":"2023-09-25T15:16:51","modified_gmt":"2023-09-25T15:16:51","slug":"ga-drl-is-managing-complex-vehicle-tasks-using-ai","status":"publish","type":"post","link":"https:\/\/mlnews.dev\/ga-drl-is-managing-complex-vehicle-tasks-using-ai\/","title":{"rendered":"Discover How GA-DRL Is Managing Complex Vehicle Tasks: Using AI In Dynamic Vehicular Clouds"},"content":{"rendered":"\n

Advanced research on GA-DRL, allows different vehicles to work together to complete difficult tasks. Vehicles complete complex tasks efficiently such as delivering packages, managing traffic issues and resolving routing problems. The main aim of this research is to enhance the capabilities of smart vehicles. This research was done by Zhang Liu<\/a>, Lianfen Huang<\/a>, Zhibin Gao<\/a>, Manman Luo<\/a>, Seyyedali Hosseinalipour<\/a>, and Huaiyu Dai<\/a> and published in the College of Information Engineering, Ningxia University one of the top Chinese Universities in Yinchuan, China.<\/p>\n\n\n

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\"VC-assisted
VC-assisted cooperative DAG task scheduling scenario.<\/em><\/figcaption><\/figure><\/div>\n\n\n

The GA-DRL approach is used to transform the world with the help of AI. It focuses on task scheduling and resource allocations and improves decision-making ability vehicles. Their seamless coordination allows them to achieve complex goals, by enhancing the productivity of the system.<\/p>\n\n\n\n

Highlighting Background Of AI Systems In Vehicles<\/strong><\/h2>\n\n\n\n

Until now, vehicle systems perform simple tasks. They handle simple tasks such as delivering packages and navigating from one place to another using a predefined route. They lack collaborated and optimized systems.<\/p>\n\n\n\n

Previous model perform tasks independently. They lack up-to-date data which reduces their productivity. Past research focused on making transportation simple for users. To surpass this limitation a new platform is utilized known as Vehicular clouds. This new research provides vehicles with scalability in real-time processing. This approach overcomes the previous limitation, faced by users, providing them with efficient results in real-time scenarios.<\/p>\n\n\n\n

GA-DRL Approach Is Managing Complex Vehicle Tasks For Everyone<\/strong><\/h2>\n\n\n\n

The present research use optimize approach by collaborating with different vehicles. They share information to know road hazards, weather conditions, and traffic areas. This much advancement in the system is gained by implementing AI and machine learning in the system, as the system is training on different datasets to operate efficiently. The vehicle has optimized routing and worked on improving user experience. It has transformed the world from simple means of transportation to advanced means of transportation. <\/p>\n\n\n

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\"Vehicle
Vehicle navigation system<\/figcaption><\/figure><\/div>\n\n\n

This allows them to perform autonomous tasks. Analyzing complex problems allows them to lead with optimal solutions. It has revolutionized the world to perform up-to-date tasks such as assisting drivers while driving, pinpointing areas with traffic. It analyzes traffic areas from a large distance, which helps them to find the path with less traffic. Routes with less traffic are highlighted to save time.<\/p>\n\n\n\n

This result shows GA-DRL’s performance is outstanding in completing complex tasks. GA-DRL approach was powerful to learn, adapt and execute the operation, in terms of efficiency and effectiveness.<\/p>\n\n\n\n

Future Of AI In Vehicles<\/strong> <\/h2>\n\n\n\n

Future work will focus on refining decision-making ability. Using a hybrid approach along GA-DRL to obtain high-efficiency results. This will allow them to integrate advanced technology and Machine learning for outstanding performance. It will manage resources without compromising its performance. In future delays will be reduced to perform concurrent tasks. A security policy will be used to secure the privacy of the vehicles.<\/p>\n\n\n

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\"GA-DRL<\/figure><\/div>\n\n\n

This research paper is available on arxiv.org<\/a>. You can check their details from there. To check how GA-DRL algorithm works, you can view the GitHub<\/a> code from here. The implementation of this research is not coded yet. Which means the code is not open source. It is a close source.<\/p>\n\n\n\n

Application<\/strong><\/h2>\n\n\n\n

It’s gonna be used in a diverse environment such as:<\/p>\n\n\n\n