Perception Aware Planning

Perception-aware planning integrates environmental perception directly into the planning process for autonomous systems, aiming to generate safer, more efficient, and robust actions. Current research emphasizes using deep learning models, such as transformers and neural networks, to predict and incorporate sensor data (e.g., LiDAR, vision) into trajectory generation and decision-making, often incorporating logical constraints or human-like decision logic for improved interpretability and reliability. This approach is crucial for advancing autonomous driving, robotics, and UAV navigation, enabling systems to adapt to dynamic environments and make informed decisions based on real-time sensory input. The development of efficient algorithms and datasets for training and evaluating these systems is a key focus.