Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks

被引:16
作者
Ali, Mohammed Hasan [1 ]
Jaber, Mustafa Musa [2 ]
Alfred Daniel, J. [3 ]
Vignesh, C. Chandru [4 ]
Meenakshisundaram, Iyapparaja [5 ]
Kumar, B. Santhosh [6 ]
Punitha, P. [7 ]
机构
[1] Imam Jaafar Al Sadiq Univ, Fac Informat Technol, Comp Tech Engn Dept, Baghdad, Iraq
[2] Al Turath Univ Coll, Dept Med instruments Engn Tech, Baghdad 10021, Iraq
[3] Karpagam Acad Higher Educ, Coimbatore, India
[4] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India
[5] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, India
[6] Guru Nanak Inst Technol, Dept Comp Sci & Engin, Hyderabad, Telangana, India
[7] SNS Coll Technol, Dept Comp Sci & Engn, Coimbatore, India
关键词
Autonomous Vehicles; Decision-Making; Mixed-Precision Neural Networks; Neural Networks; Self-Determination Theory; ARTIFICIAL-INTELLIGENCE; OPTIMIZATION; NAVIGATION;
D O I
10.1007/s11042-023-14375-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The safe human-level decision-making for expressing the autonomous vehicles and the estimation of the eco vehicles has been proposed for the motion control for the driving behavior. For efficient decision-making, the sensor-based tracking of the autonomous machine for blockchain is to be done. The sensor-based history recording management has to propose, and storing the vehicle's traveling history must be managed. For security reasons, the blockchain is done. Self-determination theory and energy-efficient mixed-precision neural networks are used in autonomous vehicles' decision-making, and this technique is used in making moral decisions. The self-determination theory is used in creating the vehicles traveling steps using the innovative signal delivery system of the autonomous vehicles. The energy-efficient mixed-precision neural networks are used in managing the problem that travels the signal to the vehicles using the mixed-precision neural networks. The vehicle network has been made more efficient for storing the data of the mixed precision value and its neural network from autonomous vehicles. Here 80% of the precision value is raised compared to the previous days. In previous days, 20% of the precision has been calculated in autonomous vehicles. Comparing this, 60% of the precision value has been raised in traveling history. According to these variations, 40%-50% of autonomous vehicles' data transmission that delivers the neural network has been proposed. By improving autonomous vehicles, the efficiency of mixed precision neural networks is the decision-making for efficient precision.
引用
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页数:24
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