The integration of connected autonomous vehicles (CAV) on open roads has gained significant progress in address- ing road safety. These vehicles use advanced sensor technology to perceive and react to the road environment, reducing accident risks. Despite these advancements, limitations persist in their perception capabilities. To overcome these limitations, interest is growing in using Unmanned Aerial Vehicles (UAVs) for traffic surveillance, offering extensive coverage and enhanced responsiveness over fixed sensors. In this article, by tackling an optimization problem in road safety using Particle Swarm Optimization (PSO), we particularly focus on a situation where a random flow of vehicles aims to navigate an intersection safely. We compare two scenarios with and without the assistance of an UAV: one where vehicles autonomously manage their speed, and another one where an UAV improve traffic management. Simulation results underscore the pivotal role of drone-assisted vehicles in enhancing road safety, compared to sensors embed- ded within CAVs. Towards the end of the article, we explore the efficiency of the drone strategy by addressing the issue of delay in the acceptance and implementation of optimal speed instructions, comparing scenarios with and without this delay.