Self-Discovered Intention-aware Transformer for Multi-modal Vehicle Trajectory Prediction
Summary: arXiv:2604.07126v1 Announce Type: cross
Abstract
Predicting vehicle trajectories plays an important role in autonomous driving and Intelligent Transportation Systems (ITS) applications. Despite the development of multiple deep learning algorithms designed for trajectory prediction, many of these methods rely on specific graph structures, such as Graph Neural Networks, or require explicit intention labeling. These dependencies can limit their flexibility and adaptability. In this study, we introduce a novel, pure Transformer-based network that incorporates multiple modalities while considering the influence of neighboring vehicles.
Key Features of the Proposed Model
- Dual Track System: The model employs two separate tracks. One track is dedicated to predicting vehicle trajectories, while the other assesses the likelihood of each vehicle’s intention based on surrounding traffic.
- Separation of Modules: By distinctly separating the spatial module from the trajectory generation module, our design enhances overall model performance and provides a clearer framework for understanding vehicle behaviors.
- Ordered Group Learning: The model is capable of learning an ordered group of trajectories by predicting residual offsets among K distinct trajectories, allowing for more nuanced predictions.
Advantages Over Existing Methods
One of the primary advantages of our proposed approach is its flexibility. Traditional methods often struggle with the limitations imposed by specific structures or the necessity of labeled intentions. In contrast, our Transformer-based model can dynamically adapt to various traffic scenarios without being constrained by these factors.
Experimental Results
We conducted extensive experiments to evaluate the performance of our model against several benchmark datasets. The results indicate a significant improvement in trajectory prediction accuracy compared to state-of-the-art methods. The dual track design not only enhances the precision of trajectory forecasts but also allows for a better understanding of the intentions behind vehicle movements.
Conclusion
In conclusion, our self-discovered intention-aware Transformer model marks a significant advancement in the field of vehicle trajectory prediction. By leveraging a multi-modal approach and separating the trajectory generation from spatial considerations, we have demonstrated that our model can provide more accurate and flexible predictions. This research opens new avenues for further exploration in autonomous driving technology and ITS applications.
Future Work
Looking ahead, we aim to refine our model further by integrating additional data sources, such as real-time sensor inputs and environmental conditions. We believe that enhancing the model’s input diversity will lead to even more robust predictions and a greater understanding of complex traffic scenarios.