  Adaptive Cruise Control ACC can change the speed of the ego vehicle to
maintain a safe distance from the following vehicle automatically. The primary
purpose of this research is to use cutting-edge computing approaches to locate
and track vehicles in real time under various conditions to achieve a safe ACC.
The paper examines the extension of ACC employing depth cameras and radar
sensors within Autonomous Vehicles AVs to respond in real time by changing
weather conditions using the Car Learning to Act CARLA simulation platform at
noon. The ego vehicle controller's decision to accelerate or decelerate depends
on the speed of the leading ahead vehicle and the safe distance from that
vehicle. Simulation results show that a Proportional Integral Derivative PID
control of autonomous vehicles using a depth camera and radar sensors reduces
the speed of the leading vehicle and the ego vehicle when it rains. In
addition, longer travel time was observed for both vehicles in rainy conditions
than in dry conditions. Also, PID control prevents the leading vehicle from
rear collisions
