Shruti Patil

Papers

Total Citations

H-Index

About

Shruti Patil is a rising researcher at the forefront of autonomous systems and computer vision, whose work bridges deep reinforcement learning and intelligent perception. Her most impactful contribution, “Improving the Performance of Autonomous Driving through Deep Reinforcement Learning” (2023, 22 citations), demonstrates how reinforcement learning can be scaled with deep learning to solve complex, real-world navigation problems—pushing autonomous vehicles toward higher-level situational awareness. In her subsequent work, “Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques” (2024, 11 citations), Patil introduces a hybrid framework that fuses color-based cues with machine learning classifiers, offering a computationally efficient path to robust object recognition for robotics and security systems. Though early in her career, her citation trajectory signals growing influence, particularly in applied AI. Patil’s research is notable for its practical orientation: she tackles the bottleneck of perception in autonomous driving by integrating reinforcement learning with vision pipelines. Her work not only advances algorithmic foundations but also points toward deployable, color-aware recognition systems—a valuable contribution for students and engineers seeking to build safer, more perceptive autonomous agents.