An efficient intelligent task management in autonomous vehicles using AIIOT and optimal kernel adaptive SVM

The fast evolution of artificial intelligence (AI) and the IoT gained more interest in the development of autonomous vehicles. The main challenges faced by autonomous car manufacturers are high computation costs and the lag of intelligent task management systems. The accident rates created by autonomous vehicles are increasing rapidly due to their unrestrained traffic, inaccurate location, and mapping methods. So, secure driving becomes the main concern in self-driving vehicle design. Moreover, the inadequate battery life and computation power made the system complex to minimize execution time as well as resource computation. Therefore, to handle all these complications, an intelligent task-managing system for autonomous vehicles is proposed in this paper. In this, each task is optimally executed by invoking the supervised resource predictor kernel data adaptive support vector machine-based multimodal bacterial foraging (KDASVM-MBF) method. The KDASVM-MBF intelligent task scheduling method is proposed to distribute all the tasks to the suitable processor based on central processing unit (CPU) usage and emergency. The proposed model is implemented in Python IDE-version 3.8 and examined using two multicore processors (Nvidia and AIIoT). The potential capability of the introduced type is evaluated by computing the performance methods such as response time, resource utilization, CPU utilization, execution time, prediction accuracy, and task miss rate. The experimental results reveal that the established KDASVM-MBF method accomplishes prediction accuracy of about 97% and 98% for Nvidia and AIIoT processors respectively with minimum task miss rate and execution time. [Display omitted] •The fast evolution of AI and IoT gained interest in developing autonomous vehicles.•Accident rate by autonomous vehicles are increasing due to unrestrained traffic.•Intelligent task managing system for autonomous vehicle is proposed in this paper.•KDASVM-MBF intelligent task scheduling method is proposed to distribute all the task.•The model is implemented in Python 3.8 and examined using two multicore processors.