Evaluating Autonomous Vehicle Performance: Integrating Data Analytics in Simulated Environments

Chinmay Shripad Kulkarni, Siva Karthik Devineni

Abstract: The evolution of autonomous vehicles (AVs) has been a remarkable journey of technological advancement, reshaping the landscape of transportation and mobility. This paper delves into the critical role of data analytics in simulated environments for evaluating the performance of autonomous vehicles. Drawing from a wealth of knowledge encapsulated in various scholarly books, we explore the multifaceted dimensions of AV performance assessment. This analysis includes understanding human-machine interactions, addressing AVs' technical, legal, and social aspects, and evaluating their vulnerabilities and resilience against potential risks. The paper aims to understand the current state, challenges comprehensively, and future directions in autonomous vehicle technology, focusing on integrating data analytics in simulated environments for performance evaluation.

Keywords: Autonomous Vehicles (AVs), Data Analytics, Simulation, Sensor Technology, Machine Learning