Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set
  Prediction
Wenbo Shao Jiahui Xu Wenhao Yu Jun Li Hong Wang
In the rapidly evolving field of autonomous driving, accurate trajectory
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, enhancing
trajectory prediction capabilities. This method 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 main contributions of this research include: 1) A novel DOS prediction
model tailored for complex scenarios, augmenting traditional trajectory
prediction; 2) The development of unique DOS representations and evaluation
metrics; 3) Extensive validation through experiments, demonstrating enhanced
performance and adaptability. This research contributes to the advancement of
safer and more efficient intelligent vehicle and transportation systems.