MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

Abstract

Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the feature. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

“…In [81,86,87], an occupancy grid map is constructed using the target vehicle's or pedestrian's position as its center. This map is then employed to aggregate the hidden states of all adjacent agents.…”

“…Subsequently, this tensor is employed in conjunction with the spatial latent state of the target agent as the primary input for the LSTM network utilized in the trajectory prediction process. In [86], Cheng et al introduced a circular polarization occupancy representation. This method utilizes the orientation and distance of the agents relative to the target pedestrian to define the cells that are considered occupied.…”