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

翻译：自适应巡航控制（ACC）能够自动调整自车速度，以保持与前车的安全距离。本研究的主要目的是运用前沿计算方法，在不同条件下实时定位与追踪车辆，从而实现安全的ACC。论文探讨了在自动驾驶车辆（AVs）中采用深度摄像头和雷达传感器扩展ACC功能，通过Car Learning to Act（CARLA）仿真平台，在正午时分根据天气变化实现实时响应。自车控制器的加速或减速决策取决于前车的速度以及与其保持的安全距离。仿真结果表明，采用比例-积分-微分（PID）控制的自动驾驶车辆，利用深度摄像头和雷达传感器，在雨天能够降低前车与自车的速度。此外，观察到雨天条件下两车的行驶时间均长于干燥条件。同时，PID控制可有效防止前车发生追尾事故。