Determine the Decision Driving Strategy of an Autonomous Vehicle

Autonomous vehicles require sophisticated decision-making strategies to navigate complex environments safely and efficiently. This paper presents a novel approach for determining the decision-driving strategy of an autonomous vehicle using a combination of genetic algorithm and Random Forest techniques. The proposed methodology leverages genetic algorithm to optimize the parameters of a Random Forest model, which acts as the decision-making engine for the autonomous vehicle. The genetic algorithm is employed to evolve the hyperparameters of the Random Forest model to maximize performance metrics such as accuracy, reliability, and robustness. The dataset used for training and testing the model consists of real-world driving scenarios, including diverse road conditions, traffic patterns, and environmental factors. Experimental results demonstrate the effectiveness of the proposed approach in generating decision-driving strategies that enable the autonomous vehicle to make informed and safe decisions in various driving situations.