Design of an Efficient Mathematical Optimization Engine for Solving Autonomous Driving Performance for Urban Traffic Conditions

Due to the rapid development of autonomous driving technology, efficient mathematical optimization engines are required to improve the performance of autonomous vehicles in urban traffic scenarios. We propose a novel method for fine-tuning an autonomous driving model using Neural Networks (NN) and Genetic Algorithms (GA) in order to reduce accidents, increase fuel efficiency, and decrease operational delays. This work is required to ensure the safety, energy efficiency, and performance of autonomous vehicles in urban traffic scenarios. Integrating NN and GA, we optimize the parameters of autonomous driving model sets using machine learning and evolutionary computations. This study has numerous applications, our strategy decreases accidents, thereby enhancing road safety and decreasing injuries. By reducing carbon emissions, fuel efficiency improves the environmental impacts. Decreased operational delay enhances traffic flow and decreases congestions. It can be utilized for autonomous urban transportation, public transport, delivery services, and logistics. By fine-tuning the autonomous driving model for urban traffic conditions, our strategy addresses the unique challenges of densely populated areas with complex road networks and unpredictable traffic patterns. Complementarity between NN and GA justifies their use. Neural Networks are capable of recognizing intricate input-output relationships and learning complex patterns from large datasets. Genetic Algorithm utilizes natural evolution to determine the optimal parameters. We use the NN's learning ability and the GA's search for optimal solutions to optimize the performance of the autonomous driving model process. We compared our optimized model of autonomous driving to more recent models. Multiple metrics showed significant improvement, for instance, accidents decreased by 8.5%, enhancing pedestrian and automobile safety levels. The increase in fuel efficiency by 12.4% has made transportation more sustainable. Reduced operational delay by 2.5% improved traffic flow and travel delays.