Adaptive Optimizer for Speed Control in Smart Vehicles: Comparative Analysis of Object Detection Algorithms and Sensor Fusion Techniques

This research introduces an innovative Adaptive Optimizer for speed control in smart vehicles, integrating advanced object detection algorithms (YOLOv3, R-CNN, SSD, Faster R-CNN) with sophisticated sensor fusion techniques (Kalman Filter, Particle Filter, Deep Learning Fusion). The Adaptive Optimizer dynamically selects the most suitable algorithms based on real-time environmental data, including lighting conditions (day, dusk, night), speed (low, medium, high), and weather conditions (clear, rainy, foggy). This selection ensures optimal performance and safe navigation by adjusting vehicle speed according to detected conditions. The system's effectiveness is evaluated using key metrics such as accuracy, processing time, and computational complexity, along with regression models to analyze errors and computation times. A feedback loop from the final speed control stage informs the optimizer, enhancing decision-making. Our findings highlight the tradeoffs between speed and accuracy, emphasizing the importance of adaptive and probabilistic decision-making for enhanced vehicle safety and efficiency. This adaptive approach is crucial for the development of smart vehicles, ensuring they can respond effectively to varying environmental challenges.