Using a relative coordinate system instead of a global one can have significant implications for the model's ability to generalize across different scenarios. One potential advantage of a relative coordinate system is that it can help reduce prediction bias towards specific scenes or locations. By anchoring the predictions relative to the target agent's position and orientation, the model may learn more generalized patterns of behavior that are applicable across various environments.
However, there are also challenges associated with using a relative coordinate system. One concern is the potential loss of absolute spatial information, which could impact the model's ability to understand global scene context. This may lead to difficulties in predicting interactions between agents in unfamiliar or diverse environments.
To address these implications, the toolbox could offer users the flexibility to switch between relative and global coordinate systems based on the specific requirements of their prediction tasks. Additionally, the toolbox could include modules for learning scene-specific transformations that map relative coordinates to global coordinates, enabling the model to adapt to different scenarios while retaining spatial context. By providing support for both relative and global coordinate systems, the toolbox can help researchers explore the trade-offs and benefits of each approach in trajectory prediction tasks.