Obstacle Trajectory

Obstacle trajectory prediction focuses on accurately forecasting the future paths of moving objects, crucial for safe autonomous navigation in dynamic environments like construction sites or roadways. Current research emphasizes robust methods for handling uncertainty in obstacle movement, often employing probabilistic models like Kalman filtering or Markov Decision Processes, and incorporating these predictions into trajectory optimization algorithms such as Model Predictive Control (MPC) or B-spline-based approaches. This field is vital for advancing the safety and reliability of autonomous systems, impacting diverse applications from aerial robotics and autonomous driving to warehouse automation.