KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced
  Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Chuheng Wei Guoyuan Wu Matthew J. Barth Amr Abdelraouf Rohit Gupta Kyungtae Han
Reliable prediction of vehicle trajectories at signalized intersections is
crucial to urban traffic management and autonomous driving systems. However, it
presents unique challenges, due to the complex roadway layout at intersections,
involvement of traffic signal controls, and interactions among different types
of road users. To address these issues, we present in this paper a novel model
called Knowledge-Informed Generative Adversarial Network (KI-GAN), which
integrates both traffic signal information and multi-vehicle interactions to
predict vehicle trajectories accurately. Additionally, we propose a specialized
attention pooling method that accounts for vehicle orientation and proximity at
intersections. Based on the SinD dataset, our KI-GAN model is able to achieve
an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error
(FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When
the prediction window is extended to 9 seconds, the ADE and FDE values are
further reduced to 0.11 and 0.26, respectively. These results demonstrate the
effectiveness of the proposed KI-GAN model in vehicle trajectory prediction
under complex scenarios at signalized intersections, which represents a
significant advancement in the target field.