NavigAId: A Deep Learning Framework for Real-Time Traffic Sign Interpretation with Multi-Sensor Fusion

The rapid advancement of autonomous vehicle technologies has necessitated the development of more efficient and accurate systems for real-time traffic sign interpretation. Traditional approaches predominantly rely on single-sensor data, which often suffer from limitations in accuracy and robustness under varying environmental conditions. This paper presents the NavigAId system, an advanced autonomous navigation framework leveraging a deep neural fusion model that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to interpret complex sensory data for enhanced navigational decision-making. Through a comprehensive empirical evaluation conducted across a variety of environmental conditions and traffic scenarios. The NavigAId system demonstrated superior performance, notably achieving accuracy rates exceeding 95% in clear weather conditions, both urban and highway, and maintaining robust performance in adverse weather and nighttime conditions with accuracy rates above 89%. The fusion model exhibited significant improvements over standalone CNN or RNN models, with accuracy enhancements ranging from 3.0% to 8.0% and precision improvements up to 8.6%, depending on the scenario. Particularly, in velocity prediction tasks, the system achieved a remarkable reduction in Mean Squared Error (MSE) by up to 33.3% compared to individual neural network models.