Developing Autonomous Driving Performance Through Neuro Evolutionary Training: A Simulation-Based Approach

Autonomous driving technology is poised to transform transportation systems, yet ensuring reliable and robust autonomous behavior remains a formidable challenge. This paper presents a simulation-based approach leveraging NeuroEvolution of Augmenting Topologies (NEAT) and Python programming to enhance autonomous driving performance. Through NEAT, neural networks are trained to control simulated vehicles in diverse environments generated using Pygame. Evolutionary algorithms within NEAT optimize neural network architectures and driving behaviors, enabling vehicles to adapt and refine their decision-making abilities over time. The study examines the influence of key factors such as sensor configurations, environment complexity, and training methodologies on driving performance. Comparative analysis with traditional machine learning techniques offers insights into the advantages and limitations of NEAT for autonomous driving applications. The research contributes to the advancement of autonomous vehicle technology by demonstrating the efficacy of NEAT in improving driving capabilities. Additionally, the study sheds light on the design and optimization of autonomous driving systems by exploring the impact of various parameters on performance. By simulating realistic driving scenarios and iteratively refining neural network architectures, this approach fosters the development of more competent and adaptable autonomous vehicles. The findings offer valuable guidance for researchers and practitioners seeking to enhance the reliability and efficiency of autonomous driving systems. Ultimately, the integration of NEAT with simulation-based training represents a promising avenue for advancing the state-of-the-art in autonomous vehicle technology.