Incremental learning-based real-time trajectory prediction for autonomous driving

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Introduction

• Overview of spatial-temporal challenges in autonomous driving.
• Importance of accurate and real-time trajectory prediction for surrounding vehicles.

Objectives

• Design an efficient, accurate, and interpretable unimodal trajectory prediction approach.

Methodology

Utilization of Sparse Gaussian Process Regression (SGPR)

• Benefits of SGPR
  • Scalability to large datasets
  • Computational efficiency

Translation and Rotation Transformation Strategy

• Simplification of prediction problems
• Increased robustness of the prediction module

Instant Evaluation Algorithm

• Performance assessment of predictions
• Maintenance of a streaming dataset for incremental learning

Experimental Evaluation

• Comparison with existing methods
• Metrics
  • Prediction accuracy
  • Inference time

Results & Discussion

• Superiority in prediction accuracy and computational efficiency
• Adaptability to dynamic environments through incremental learning

Conclusion

• Potential of the proposed SGPR-based approach with rotation equivalence in enhancing safety and efficiency in autonomous driving systems.
• Future directions and possible improvements.