TIGFlow-GRPO: Trajectory Forecasting via Interaction-Aware Flow Matching and Reward-Driven Optimization

1. 1Model spatio-temporal observations using interaction-aware flow matching
2. 2Incorporate social norms and scene constraints into the model
3. 3Optimize the model using reward-driven optimization to improve forecasting accuracy
4. 4Evaluate the model on real-world datasets to validate its effectiveness

Machine learning researchers and engineers working on autonomous driving or crowd surveillance systems can benefit from this approach to improve trajectory forecasting accuracy

💡 Interaction-aware flow matching and reward-driven optimization can improve trajectory forecasting accuracy

💡 Forecast human trajectories with TIGFlow-GRPO!