CACCga: Multi-Objective Optimization of CACC Controller Parameters Using NSGA-III

• Acarcio Gomes de Almeida Junior UFPE
• Abel Guilhermino da Silva Filho UFPE

Resumo

This study investigates the use of Non-dominated Sorting Genetic Algorith III to optimize the hyperparameters of Cooperative Adaptive Cruise Control (CACC) controllers, aiming to enhance performance in dynamic traffic scenarios. The proposed approach, identifies hyperparameter combinations on Pareto fronts based on the reduction of its accumulated error and overshoot, which are related to the system’s response time and stability, respectively. The proposed CACC controller, tuned with NSGA-III, achieves an accumulated error of 17.37 and an overshoot of 5.86%, outperforming other controllers in speed and accuracy when responding to variations in the leader vehicle’s speed. Moreover, it maintains a constant safe distance between vehicles, which is essential for passenger safety and comfort.

Palavras-chave: NSGA-III, CACC, Optimization Algorithm

Referências

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