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@article{173908,
        author = {Harrish K and Kamesh D and Veena K},
        title = {ADVANCED LANE DETECTION AND AUTONOMOUS NAVIGATION IN SELF-DRIVING CARS USING AI AND SENSOR FUSION TECHNOLOGIES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2467-2474},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173908},
        abstract = {This paper presents a novel approach to enhancing lane detection and autonomous navigation in self-driving cars by leveraging artificial intelligence (AI) and sensor fusion technologies. Traditional lane detection systems, reliant on a single type of sensor, often struggle with accuracy under challenging conditions such as poor lighting or adverse weather. To address these limitations, our approach integrates data from multiple sensors, including cameras, LiDAR, and radar, using a sensor fusion framework. The system employs advanced AI algorithms, particularly convolutional neural networks (CNNs), to process and analyze the fused data, enabling robust and precise lane detection even in complex environments. The autonomous navigation module utilizes the detected lane information in real-time, dynamically adjusting the vehicle’s path while ensuring stability and safety. Experimental results, conducted in both simulated and real-world conditions, demonstrate that our system significantly outperforms traditional methods in terms of accuracy and reliability. This research contributes to the development of more resilient and dependable autonomous driving systems, bringing us closer to fully autonomous vehicles capable of operating in a wide range of scenarios.},
        keywords = {},
        month = {March},
        }