  In the rapidly evolving field of autonomous driving, reliable prediction is
pivotal for vehicular safety. However, trajectory predictions often deviate
from actual paths, particularly in complex and challenging environments,
leading to significant errors. To address this issue, our study introduces a
novel method for Dynamic Occupancy Set (DOS) prediction, it effectively
combines advanced trajectory prediction networks with a DOS prediction module,
overcoming the shortcomings of existing models. It provides a comprehensive and
adaptable framework for predicting the potential occupancy sets of traffic
participants. The innovative contributions of this study include the
development of a novel DOS prediction model specifically tailored for
navigating complex scenarios, the introduction of precise DOS mathematical
representations, and the formulation of optimized loss functions that
collectively advance the safety and efficiency of autonomous systems. Through
rigorous validation, our method demonstrates marked improvements over
traditional models, establishing a new benchmark for safety and operational
efficiency in intelligent transportation systems.
