NavigAId: A Deep Learning Framework for Real-Time Traffic Sign Interpretation with Multi-Sensor Fusion
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 9 |
| Year of Publication : 2024 |
| Authors : Ch Rammohan, S MP Qubeb, Nithya Darsini P.S, N. Saikiran, Kishore Dasari, Kadiyala Vijaya Kumar |
10.14445/23488549/IJECE-V11I9P121
How to Cite?
Ch Rammohan, S MP Qubeb, Nithya Darsini P.S, N. Saikiran, Kishore Dasari, Kadiyala Vijaya Kumar, "NavigAId: A Deep Learning Framework for Real-Time Traffic Sign Interpretation with Multi-Sensor Fusion," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 9, pp. 237-252, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I9P121
Abstract:
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.
Keywords:
Autonomous navigation, Deep neural fusion model, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Environmental adaptability, Velocity prediction.
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