  The problem of multimodal intent and trajectory prediction for human-driven
vehicles in parking lots is addressed in this paper. Using models designed with
CNN and Transformer networks, we extract temporal-spatial and contextual
information from trajectory history and local bird's eye view (BEV) semantic
images, and generate predictions about intent distribution and future
trajectory sequences. Our methods outperform existing models in accuracy, while
allowing an arbitrary number of modes, encoding complex multi-agent scenarios,
and adapting to different parking maps. To train and evaluate our method, we
present the first public 4K video dataset of human driving in parking lots with
accurate annotation, high frame rate, and rich traffic scenarios.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics;Systems and Control;Systems and Control