Rajeev Sobti
Papers
2
Total Citations
11
H-Index
2
About
Rajeev Sobti is a researcher at the forefront of autonomous vehicle technology, specializing in the integration of artificial intelligence, machine learning, and computer vision for intelligent driving systems. His work centers on developing robust motion planning and simulation frameworks that bridge the gap between theoretical AI and real-world vehicular control. Sobti’s most notable contribution is a novel hybrid framework that synergizes deep reinforcement learning with imitation learning, enabling autonomous vehicles to navigate complex, dynamic environments with enhanced safety and adaptability—a critical advancement for smart city ecosystems. His foundational paper on scenario-based simulation using neural networks, which has garnered 8 citations, laid the groundwork for testing intelligent driving functions in controlled, high-fidelity virtual environments. More recently, his 2024 work on hybrid motion planning, already cited 3 times, demonstrates his ongoing impact in addressing the core challenge of decision-making under uncertainty. By combining data-driven learning with classical planning, Sobti’s research directly contributes to the evolution of safer, more accessible self-driving cars, marking him as an emerging voice in the field of autonomous systems and intelligent transportation.
Research Focus
Key Achievements
Top Papers
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