Incremental Learning-Based Real-Time Trajectory Prediction for Autonomous Driving via Sparse Gaussian Process Regression

In the context of spatial-temporal autonomous driving, the accurate and real-time trajectory prediction of the surrounding vehicle (SV) is crucial. This paper aims to design an efficient, accurate, and interpretable unimodal trajectory prediction approach. To achieve this objective, we employ Sparse Gaussian Process Regression (SGPR), which enables large dataset learning and efficient inference of future trajectories. This approach ensures accurate predictions while maintaining high computational efficiency. To further enhance the robustness of the prediction module, we propose the translation and rotation transformation strategy, which effectively simplifies the prediction problem. Additionally, we utilize an instant evaluation algorithm to assess the prediction performance and maintain a streaming dataset for incremental learning, capable of adapting to dynamic driving environments. In our experimental evaluation, we compare our proposed trajectory prediction approach with a series of existing methods. The results demonstrate that our work achieves superior prediction accuracy while requiring less inference time. It is noteworthy that, the proposed SGPR-based trajectory prediction approach with rotation equivalence is able to swiftly infer and incrementally learn from dynamic environments, which makes it a promising tool for enhancing safety and efficiency in autonomous driving systems.