Year: 2026
Type: article
Abstract: Cut-in maneuvers pose a major challenge for Adaptive Cruise Control (ACC) systems because they require rapid re-identification of the lead vehicle and immediate adjustment of longitudinal control to maintain safety. Conventional ACC controllers often struggle to respond quickly and smoothly under these abrupt disturbances. This study proposes the use of the Crow Search Algorithm (CSA) to optimize key ACC control parameters, enabling improved adaptability and stability during cut-in events. CSA is employed to tune the controller gains through an iterative search process that balances exploration and exploitation. Simulation experiments are conducted to evaluate the influence of iteration numbers and population sizes on optimization performance. The optimized ACC system is validated through a cut-in scenario, assessing relative distance error, velocity error, and recovery behavior after the disturbance. The results show that CSA significantly enhances the controller’s ability to reduce transient errors, stabilize inter-vehicle distance, and restore speed in a smooth and safe manner. These findings demonstrate that CSA is a promising metaheuristic for improving ACC robustness in highly dynamic traffic environments. Future extensions include testing in multi-scenario conditions, incorporating cooperative vehicle-to-vehicle communication, and integrating hybrid optimization methods to further enhance real-time performance.
Institution: State University of Semarang
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7164322816