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About

Parampreet Kaur is a researcher at the forefront of autonomous vehicle technology, specializing in the integration of artificial intelligence, computer vision, and sensor fusion to enable safer, smarter self-driving systems. Her work spans critical areas including Simultaneous Localization and Mapping (SLAM), motion planning, and intelligent driving simulation. Kaur’s most cited paper, “Scenario-Based Simulation of Intelligent Driving Functions Using Neural Networks” (2018, 8 citations), established a foundational approach for testing autonomous systems in virtual environments. She further advanced the field with a comprehensive review of SLAM techniques (2023, 5 citations) and a comparative study of automotive sensor technologies (2021, 4 citations), providing essential guidance for sensor selection in unmanned driving. Her recent novel hybrid framework for motion planning (2024, 3 citations), which integrates reinforcement and imitation learning, represents a significant step toward more adaptive and human-like driving behavior. Kaur’s research has accumulated over 23 citations, demonstrating growing influence in the autonomous driving community. Her work is particularly notable for bridging theoretical advances with practical simulation and sensor validation, making her a key contributor to the development of reliable, intelligent vehicles for future smart cities.