Evaluation and Optimization of Adaptive Cruise Control in Autonomous Vehicles using the CARLA Simulator: A Study on Performance under Wet and Dry Weather Conditions

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.