Mahamkali Bhavani Shankar

National Institute of Technology Karnataka

Papers

Total Citations

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About

Mahamkali Bhavani Shankar is a researcher whose work sits at the intersection of computer vision and autonomous navigation, with a primary focus on lane detection algorithms—a critical enabler for fully-assistive and autonomous driving systems. His most notable contribution is a novel, pragmatic approach to lane detection that leverages Google Street View imagery and a convolutional neural network (CNN) based on the SegNet encoder-decoder architecture. This work, published in 2019, has garnered a combined 28 citations, reflecting its relevance in the rapidly evolving field of self-driving technology. By demonstrating how deep learning can robustly identify lane markings from real-world street-level data, Shankar’s research addresses a fundamental challenge in vehicle autonomy: reliable perception under dynamic conditions. His approach stands out for its practicality, offering a scalable solution that could be integrated into real-time navigation systems. For students and researchers exploring computer vision applications in transportation, Shankar’s work provides a clear example of how encoder-decoder architectures can be adapted for precise, pixel-level segmentation tasks, making his contributions a valuable reference point for advancing autonomous vehicle safety and efficiency.