Tracking and Estimation of Ego - Vehicle

Abstract

In future, Autonomous vehicles will play a significant part in the urban transportation networks, since they increase safety and productivity with expanded accessibility to improve road efficiency, and provide a favorable environmental impact. Major goal of this research is to use edge computing techniques to locate and track automobiles in real-time circumstances. The Extended Kalman Filtering algorithm is used to perform the localisation. The latency has been minimised because to the use of edge computing. The ultimate goal of designing a self-driving cars using edge computing system is to provide more computing power, with reduction in redundancy, and more security. Motion planning methods include searching for a path to follow, avoiding obstacles, and determining the optimal trajectory path that ensures safety and efficiency when conveying persons or commodities from an origin to a destination.