COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving
Summary: arXiv:2604.00402v1 Announce Type: cross
Introduction
As the field of autonomous driving continues to evolve, the need for robust trajectory prediction models becomes increasingly critical for ensuring safety. Accurate predictions of surrounding agents’ movements are essential for navigating complex traffic situations. However, most publicly available datasets, such as the Waymo Open Motion Dataset and Argoverse, predominantly feature Western road environments. This limitation introduces a significant challenge when these models are deployed in regions with distinct traffic patterns and behaviors, such as South Korea.
The Challenge of Domain Discrepancy
The geographical and infrastructural differences between regions can lead to performance degradation in state-of-the-art models that are trained primarily on Western data. The unique traffic dynamics, laws, and driving cultures in countries like South Korea necessitate a tailored approach for trajectory prediction. In this study, we delve into the adaptability of Query-Centric Trajectory Prediction (QCNet) as it transitions from U.S.-based data to the Korean road environment.
Methodology
To address the challenge of domain adaptation, we utilize a Korean autonomous driving dataset and evaluate four distinct training strategies:
- Zero-shot transfer: Deploying the model without any additional training on the target domain.
- Training from scratch: Developing the model entirely on the new dataset without prior knowledge.
- Full fine-tuning: Adjusting the entire model based on the Korean data.
- Encoder freezing: Keeping the encoder parameters static while fine-tuning the decoder.
Results
The experimental results highlighted a significant improvement in prediction performance when pretrained knowledge was utilized. Notably, the approach of selectively fine-tuning the decoder while freezing the encoder provided the most effective balance between accuracy and training efficiency. This method resulted in a reduction of prediction error by over 66% compared to the training from scratch approach.
Conclusion
This study offers valuable insights into effective transfer learning strategies for deploying trajectory prediction models in diverse geographic contexts. By demonstrating the adaptability of QCNet in the Korean autonomous driving landscape, we emphasize the importance of context-aware methodologies in enhancing the safety and reliability of autonomous vehicles across various environments.
Future Work
Ongoing research will focus on further refining these strategies and exploring additional datasets to broaden the applicability of trajectory prediction models globally. Understanding the intricacies of different driving environments will continue to be a pivotal aspect in advancing autonomous driving technologies.