Trajectory prediction via learning motion cluster patterns in curvilinear coordinates

Aotian Wu, Tania Banerjee, Anand Rangarajan, Sanjay Ranka

Abstract:

A high proportion of crashes happen at or near
intersections. To improve intersection safety, trajectory prediction of vehicles has been studied intensively, mostly for
automated vehicle (AV) and advanced driver assistance system
(ADAS) applications. These approaches with vehicle-mounted
sensors still suffer from limited detection range or occluded
views. In this work, we propose to perform trajectory prediction on surveillance cameras. As Vehicle-to-Infrastructure
(V2I) technology enables low-latency wireless communication,
warnings from our prediction algorithm can be sent to vehicles
in real-time. Our approach consists of an offline learning
phase and an online prediction phase. The offline phase learns
common motion patterns from clustering and finds prototype
trajectories for each cluster, and updates the prediction model.
The online phase predicts the future trajectories for incoming
vehicles assuming they follow one of the motion patterns learned
from the offline phase. We adopted a long short-term memory
encoder-decoder (LSTM-ED) model for the task of trajectory
prediction. We also explored the usage of a Curvilinear Coordinate System (CCS), which utilizes the learned prototype
and simplifies the trajectory representation. Our model is also
able to handle noisy data and variable-length trajectories. Our
proposed approach outperforms the baseline Gaussian Process
(GP) model, and shows sufficient reliability when evaluated on
collected intersection data.