Adaptive Cruise Control for Electric Vehicles: A Performance Study Using CARLA | ||||
| The International Conference on Applied Mechanics and Mechanical Engineering | ||||
| Volume 22, Issue 22, October 2025, Page 1-13 PDF (977.59 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.1088/1757-899X/973/1/amme.2025.449033 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Assem Meghawer  1; Mohamed Fawzy El-Khatib2; Mohamed I. Abu El-Sebah3; Yasser I. El-Shaer1
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| 1Mechanical Engineering Department, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Smart-Village Branch, Cairo, Egypt. | ||||
| 2Mechatronics and Robotics Engineering Department, Faculty of Engineering, Egyptian Russian University (ERU), Cairo 11829, Egypt. | ||||
| 3Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo, 11843, Egypt. | ||||
| Abstract | ||||
| Adaptive cruise control (ACC) plays a crucial role in enhancing safety and efficiency in autonomous and electric vehicles by regulating vehicle speed and maintaining safe following distances. This study evaluates ACC performance under two distinct driving scenarios: steady-speed following, where the ego vehicle must track a lead vehicle maintaining a constant velocity, and stop-and-go traffic conditions, which require frequent acceleration and deceleration to adapt to dynamic traffic flow. By implementing a two-level proportional integral derivative (PID) control system within the CARLA simulation environment, we conduct a detailed assessment of key performance metrics, including following distance accuracy, control stability, and deviation from ISO-defined safe distances. In the steady-speed scenario, the system is expected to achieve smooth speed tracking with minimal deviations, ensuring a stable and predictable driving experience. In contrast, the stop-and-go scenario poses greater challenges, as the ego vehicle must respond rapidly to sudden changes in traffic conditions while maintaining control precision. The study explores how the controller adapts to these variations, analyzing its ability to minimize excessive braking, maintain appropriate acceleration, and optimize safety without compromising comfort. Our findings highlight the system's capability to maintain stability in steady-speed conditions while demonstrating adaptability in dynamic stop-and-go situations. This research underscores the importance of refining ACC strategies to accommodate diverse driving conditions, ultimately improving the performance and reliability of autonomous vehicle control systems in real-world environments. | ||||
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