Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction.

Area of Science:

• Autonomous Systems
• Robotics
• Computer Vision

Background:

• Autonomous vehicles require robust motion forecasting for safe navigation, especially in complex environments like roundabouts.
• Existing trajectory prediction models face challenges in generalizing across diverse driving scenarios and datasets.

Purpose of the Study:

• To develop and evaluate a novel trajectory prediction framework for autonomous vehicles operating in complex roundabout scenarios.
• To assess the generalizability of the proposed model across different data distributions, including varying road configurations and recording times.

Main Methods:

• Utilized the rounD dataset for analysis.
• Developed a trajectory prediction framework incorporating Gated Recurrent Unit (GRU) networks and graph-based modules.
• Conducted extensive experiments to evaluate model performance under diverse data distributions.

Main Results:

• The proposed framework demonstrates potential for advancing motion forecasting in roundabout environments.
• Investigated the impact of data distribution variations on prediction accuracy and model robustness.
• Identified key challenges and insights related to domain generalization in autonomous vehicle trajectory prediction.

Conclusions:

• The study provides valuable insights into the domain generalization capabilities of motion forecasting models for autonomous vehicles.
• Highlights the importance of diverse datasets for training robust trajectory prediction systems.
• Offers a foundation for developing more reliable autonomous navigation systems in complex traffic scenarios.