Abstract
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
Abstract (translated)
自适应巡航控制（ACC）可以根据预设的速度自动改变车辆的自适应速度，以保持与后车安全距离。本研究的主要目的是利用尖端计算方法在各种情况下实时定位和跟踪车辆，以实现安全ACC。论文检查了在自动驾驶车辆（AV）中使用深度相机和雷达传感器扩展ACC，通过使用Car Learning to Act CARLA仿真平台在中午实时响应天气条件。自车控制器决定加速或减速取决于前车的速度和与该车辆的安全距离。仿真结果表明，使用深度相机和雷达传感器的自动驾驶车辆在下雨时，自车和前车的速度都会降低。此外，在雨天观察到的车辆行驶时间比干燥条件下更长。此外，PID控制还可以防止前车发生碰撞。
URL
https://arxiv.org/abs/2405.01504
