Model evaluation demonstrates improved trajectory prediction accuracy and reduced inference latency in autonomous driving benchmarks, highlighting its utility for real-time navigation.
Key Points
To develop an efficient conditional diffusion framework that balances trajectory diversity, prediction accuracy, and inference speed for multi-agent motion forecasting in autonomous driving.
Designed a two-stage framework employing a probabilistic initializer to generate proposal trajectories alongside adaptive denoising steps.
Integrated a group-aware conditional encoder to capture dynamic multi-agent interactions and direct the diffusion process.
Evaluated forecasting accuracy and computational efficiency on the INTERACTION and Argoverse benchmark datasets.
Achieved state-of-the-art accuracy on the INTERACTION dataset, decreasing minADE by up to 9% and minFDE by up to 10%.
Demonstrated reduced inference time in ablation experiments while maintaining prediction precision across multi-agent traffic scenarios.