In this paper, we present a comparative study of Event processing that has attained much attention in recent years in the field of autonomous driving in adverse conditions. Frame-based camera simulators for different events capture images at prescribed times, and it is often plagued by problems during high-speed events, as motion blur can occur and fail to perform in dynamic environments. Event cameras, on the other hand, are an alternative that can capture asynchronous, sparse, and high-frequency pixel-level events at very low latency, particularly well suited for applications like autonomous driving. However, high-resolution event capture requires high-speed circuitry and accurate timing mechanisms, and makes it technically challenging and expensive to manufacture the event cameras. To overcome these challenges, several open-source simulators have been proposed to test and validate event generations. In this work, we discuss the comparison of different open-source event simulators such as ESIM, V2E, V2CE, and Recurrent Vision Transformer (RVT). We consider diverse driving conditions, a broad spectrum of sensor noise, and realistic environments for this analysis. Relevant quantitative metrics such as accuracy, temporal resolution, latency, and noise handling are also included in the justification of the selection of event simulation in real-time applications. We observe that the RVT-based event simulator is promising for autonomous driving applications.