Trajectory Prediction via Learning Motion Cluster Patterns in Curvilinear Coordinates

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.