  In this paper, we address the problem of predicting the future motion of a
dynamic agent (called a target agent) given its current and past states as well
as the information on its environment. It is paramount to develop a prediction
model that can exploit the contextual information in both static and dynamic
environments surrounding the target agent and generate diverse trajectory
samples that are meaningful in a traffic context. We propose a novel prediction
model, referred to as the lane-aware prediction (LaPred) network, which uses
the instance-level lane entities extracted from a semantic map to predict the
multi-modal future trajectories. For each lane candidate found in the
neighborhood of the target agent, LaPred extracts the joint features relating
the lane and the trajectories of the neighboring agents. Then, the features for
all lane candidates are fused with the attention weights learned through a
self-supervised learning task that identifies the lane candidate likely to be
followed by the target agent. Using the instance-level lane information, LaPred
can produce the trajectories compliant with the surroundings better than 2D
raster image-based methods and generate the diverse future trajectories given
multiple lane candidates. The experiments conducted on the public nuScenes
dataset and Argoverse dataset demonstrate that the proposed LaPred method
significantly outperforms the existing prediction models, achieving
state-of-the-art performance in the benchmarks.

Computer Vision and Pattern Recognition;Machine Learning