Rhuthik P supervised by Dr. Deepak Gangadharan received his  Master of Science –  Dual Degree in Electronics and Communication Engineering  (ECD). Here’s a summary of his research work on Resource and Power-Efficient Lane Detection Techniques for Cost-constrained Vehicular System:

 The rapid advancement of automotive technology, particularly in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs), has significantly enhanced vehicle safety and driver comfort. Central to these advancements is the task of lane detection, which enables vehicles to perceive their position on the road accurately. However, deploying robust lane detection systems faces significant hurdles, especially on the resource-constrained edge computing platforms typically found within vehicles, and critically within Electric Vehicles (EVs) where energy conservation is paramount for maximizing driving range. Existing lane detection solutions often present a stark trade-off. Deep Learning (DL) models offer superior accuracy and robustness across diverse conditions but demand substantial computational power and energy. Conversely, traditional Image Processing (IP) algorithms are lightweight and energy-efficient but frequently struggle with performance in challenging scenarios like poor lighting or complex road markings. Relying solely on either approach is often suboptimal, leading to either compromised safety due to low accuracy or reduced operational range due to high energy consumption. This necessitates intelligent strategies that can effectively manage the accuracy-resource trade-off in real time. This thesis addresses these challenges by proposing and evaluating efficient and adaptive frameworks specifically designed for lane detection on resource-constrained vehicular platforms. The contributions aim to improve both the inherent efficiency of lane detection processing and the dynamic management of available detection methods. The first contribution introduces S2P, a novel two-stage superpixel algorithm designed to enhance traditional IP-based lane detection methods like Hough Transform (HT) and Probabilistic Hough Transform (PHT). By intelligently reducing the image area requiring processing through static and dynamic superpixel mapping, S2P significantly improves the accuracy and recall of these lightweight methods (e.g., improving PHT accuracy from 0.89 to 0.95) while maintaining or even reducing power consumption, making them more viable for low-power edge devices. The second contribution presents RL-ALMS, an adaptive model selection framework employing contextual multi-armed bandits and Thompson sampling. RL-ALMS dynamically chooses between different available lane detection models (ranging from lightweight IP to computationally intensive DL) based on real-time context, including road conditions, system performance history, and crucially, the vehicle’s remaining battery state. It incorporates a novel battery-aware reward function and sustainability constraints to intelligently balance immediate detection accuracy with long-term energy preservation, ensuring journey completion. Experimental results demonstrate RL-ALMS’s effectiveness, achieving high average accuracy (90.3%) comparable to always using a DL model, while preserving significant battery reserves (81.2% after 50km) and adapting robustly to varying conditions, significantly outperforming static high-accuracy or energy-efficient baseline strategies. Both proposed solutions are evaluated using relevant datasets (TuSimple) and hardware platforms (Raspberry Pi, Jetson Nano), demonstrating their practical potential for enhancing lane detection capabilities in modern vehicles.

July 2025