Apoorv Singh

Carnegie Mellon University, PATH To Reading, Motion Control (United States)

Papers

Total Citations

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About

Apoorv Singh is an emerging researcher specializing in autonomous perception systems, with a particular focus on sensor fusion, vision transformers, and robotic detection frameworks. His work sits at the intersection of deep learning and real-world autonomous systems, addressing some of the most pressing challenges in making machines perceive their environments reliably and at scale. Singh's most influential contribution, "Transformer-Based Sensor Fusion for Autonomous Driving: A Survey" (2023), has garnered 33 citations and provides a comprehensive analysis of how transformer architectures combined with CNN-based feature encoders are redefining 3D detection pipelines. Complementing this, his "Vision-RADAR Fusion for Robotics BEV Detections: A Survey" (25 citations) tackles the critical challenge of cross-modality information fusion for scalable robotic platforms, offering researchers a valuable roadmap for building robust Bird's Eye View perception systems. His work on "Training Strategies for Vision Transformers for Object Detection" (9 citations) further demonstrates his depth in optimizing transformer models originally designed for language tasks to excel in spatial, perception-driven applications. Collectively accumulating nearly 70 citations within a single year, Singh's research is rapidly shaping how the autonomous driving and robotics communities approach next-generation perception architectures.