CVPR 2019 Poster Presented by Xu Gao 2019/07/04 TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions CVPR 2019 Poster Presented by Xu Gao 2019/07/04

Problem Definition Trajectory Prediction in Dense Heterogeneous Traffic Conditions. Inputs: Trajectories of agents Outputs: Predicted trajectories of agents

Existing Methods Dealing with homogeneous agents. Methods are evaluated on traffic videos corresponding to relatively sparse scenarios with few heterogeneous interactions. Model the interactions for all road agents in its neighborhood with equal weight.

Observations Road agents in dense traffic do not react to every road agent around them. They focus on key interactions in the field of view. A bus-pedestrian interaction differs from a pedestrian-pedestrian interaction because of the differences in shapes and sizes. Target Agent Horizon Interaction Heterogeneous Interaction

Contributions Horizon-Based Weighted Interaction. Prioritize the interactions of agents in the horizon. Heterogeneous-Based Weighted Interaction. Implicitly consider varying sizes, aspect ratios, driver behaviors, and dynamics of agents. A New Traffic Dataset TRAF. Target Agent Horizon Interaction Heterogeneous Interaction

Pipeline 2D->3D Coordinate Space Concatenation Assume positions of the road agent in the next frame following a bi-variate Gaussian distribution.

Implicit Constraints Turning Radius: Position, Velocity, Shape. Driver Behavior: Traffic Concentration. Relative Distance of a Road Agent from its neighbors.

Dataset and Metrics New TRAF dataset under dense and heterogeneous urban traffic. 50 videos of dense and heterogeneous traffic. Approximately 13 vehicles, 5 pedestrians and 2 bicycles per frame. Resolution: 1280*720. https://go.umd.edu/TRAF-Dataset. Metrics: Average Displacement Error (ADE) and Final Displacement Error (FDE).

Experimental Results TraPHic-B: Baseline Version without using any interactions. TraPHic-Ho: Baseline Version with horizon interactions TraPHic-He: Baseline Version with heterogeneous interactions. TraPHic: Final verision.

Evaluated on Sparse of Homogeneous Dataset

Results

Conclusion + Deal with trajectory prediction under dense and heterogeneous urban traffic. + Interesting constraints. - Less details. - Does not consider the perspective of the video. - Fixed hyperparameter.