Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks - Ali, Mohammed Hasan; Jaber, Mustafa Musa; Alfred Daniel, J; Vignesh, C. Chandru; Meenakshisundaram, Iyapparaja; Kumar, B. Santhosh; Punitha, P. (Multimedia tools and applications, 2025)
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.