Waypoint Prediction

Waypoint prediction focuses on accurately forecasting the future path of agents, such as autonomous vehicles or robots, within complex environments. Current research emphasizes improving prediction accuracy by incorporating diverse data modalities (e.g., visual, sensor, and temporal information) into models, often employing transformer-based architectures or discrete choice models to handle the inherent multi-modality of agent behavior and long-term goals. This research is crucial for advancing autonomous navigation and robotics, enabling safer and more efficient systems by improving path planning and obstacle avoidance. The development of large-scale datasets with human-centric annotations is also a key area of focus, facilitating the training and evaluation of more robust and reliable waypoint prediction models.