1. 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 /07/04

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

3. 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.

4. 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

5. 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

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

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

8. 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.
Metrics: Average Displacement Error (ADE) and Final Displacement Error (FDE).

9. 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.

10. Evaluated on Sparse of Homogeneous Dataset

11. Results

12. 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.