Occupancy Prediction

Occupancy prediction aims to create a 3D representation of a scene's occupied and unoccupied spaces, crucial for applications like autonomous driving and robotics. Current research emphasizes efficient model architectures, such as those based on transformers and convolutional neural networks, often incorporating bird's-eye-view representations and multi-sensor fusion to improve accuracy and speed, particularly for real-time applications. This field is significant because accurate and efficient occupancy prediction is essential for safe and reliable navigation in dynamic environments, impacting advancements in autonomous systems and related safety technologies.