The Adaptive Cruise Control for Curved Roads Using Archived Crow Search Algorithm

One of the advanced driver assistance systems (ADAS) technologies that can address the issue of high-traffic accidents is adaptive cruise control (ACC). However, a challenge arises due to the lack of control algorithm development in ACC technology that accommodates curved road conditions. This paper proposes a comprehensive solution by introducing ACC for curved roads through the utilization of a multidimensional control system model. This paper aims to implement the crow search algorithm (CSA) into the ACC technology: (1) Our objective is to apply the original crow search algorithm (OCSA) to find the most optimal values for the parameters v_err, x_err, v_x of ACC, and k_p and k_i of lateral displacement control; (2) We also implement the archived crow search algorithm (ACSA) into the control system, which is considered to have faster computation time than OCSA. Based on the obtained results, ACSA demonstrates faster computation time. The optimal values for achieving enhanced performance are found to be k_p at 0.7492, k_i at 0.6506, v_err at 0.9716, x_err at 0.9778, and v_x at 0.7012. This model was developed using MATLAB and compared to the non-optimized version. The research aims to contribute to ADAS development by addressing the optimization challenges of control algorithms for ACC parameters on curved roads. Ultimately, this solution enhances driver safety by providing more effective control in challenging road conditions.