Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling
arXiv:2602.21319v1 Announce Type: new Abstract: Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts and the inherently stochastic nature of future motion. Diffusion-based generative models have recently shown strong potential for capturing multimodal futures, yet existing approaches such as cVMD suffer from slow sampling, limited exploitation of generative diversity and brittle scenario encodings. This work introduces cVMDx, an enhanced diffusion-based trajectory prediction framework that improves efficiency, robustness and multimodal predictive capability. Through DDIM sampling, cVMDx achieves up to a 100x reduction in inference time, enabling practical multi-sample generation for uncertainty estimation. A fitted Gaussian Mixture Model further provides tractable multimodal predictions from the generated trajectories. In addition, a CVQ-VAE variant is evaluated for sc
arXiv:2602.21319v1 Announce Type: new Abstract: Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts and the inherently stochastic nature of future motion. Diffusion-based generative models have recently shown strong potential for capturing multimodal futures, yet existing approaches such as cVMD suffer from slow sampling, limited exploitation of generative diversity and brittle scenario encodings. This work introduces cVMDx, an enhanced diffusion-based trajectory prediction framework that improves efficiency, robustness and multimodal predictive capability. Through DDIM sampling, cVMDx achieves up to a 100x reduction in inference time, enabling practical multi-sample generation for uncertainty estimation. A fitted Gaussian Mixture Model further provides tractable multimodal predictions from the generated trajectories. In addition, a CVQ-VAE variant is evaluated for scenario encoding. Experiments on the publicly available highD dataset show that cVMDx achieves higher accuracy and significantly improved efficiency over cVMD, enabling fully stochastic, multimodal trajectory prediction.
Executive Summary
This article introduces cVMDx, an enhanced diffusion-based trajectory prediction framework for autonomous driving, leveraging DDIM sampling to achieve up to a 100x reduction in inference time and enabling practical multi-sample generation for uncertainty estimation. cVMDx improves efficiency, robustness, and multimodal predictive capability compared to existing approaches like cVMD. Experiments on the highD dataset demonstrate higher accuracy and significantly improved efficiency. This work showcases the potential of diffusion-based models for uncertainty-aware trajectory prediction, a critical challenge in autonomous driving. By providing tractable multimodal predictions and robust scenario encodings, cVMDx advances the field towards fully stochastic and multimodal trajectory prediction.
Key Points
- ▸ Introduction of cVMDx, an enhanced diffusion-based trajectory prediction framework
- ▸ Use of DDIM sampling for up to 100x reduction in inference time
- ▸ Improved efficiency, robustness, and multimodal predictive capability
Merits
Strength in addressing uncertainty-aware trajectory prediction
cVMDx effectively addresses the core challenge of uncertainty-aware trajectory prediction in autonomous driving, leveraging diffusion-based generative models and DDIM sampling for improved efficiency and robustness.
Robust scenario encoding and tractable multimodal predictions
The use of a CVQ-VAE variant for scenario encoding and a fitted Gaussian Mixture Model for multimodal predictions provides robust and tractable results, enabling fully stochastic and multimodal trajectory prediction.
Demerits
Limited evaluation on diverse scenarios
The article primarily evaluates cVMDx on the highD dataset, which may not capture the full range of diverse scenarios encountered in real-world autonomous driving applications.
Potential over-reliance on DDIM sampling
While DDIM sampling is instrumental in achieving improved efficiency, the article may benefit from exploring alternative sampling strategies to ensure robustness and generalizability.
Expert Commentary
The article presents a significant advancement in uncertainty-aware trajectory prediction for autonomous driving, leveraging diffusion-based generative models and DDIM sampling. While the evaluation on the highD dataset is convincing, the article would benefit from more comprehensive evaluation on diverse scenarios and exploration of alternative sampling strategies. Nevertheless, cVMDx showcases the potential of diffusion-based models for addressing critical challenges in autonomous driving, and its integration into existing systems could have a significant impact on the development of safe and efficient autonomous driving systems.
Recommendations
- ✓ Future research should focus on exploring alternative sampling strategies and more comprehensive evaluation on diverse scenarios.
- ✓ The development of cVMDx should be integrated into existing autonomous driving systems to improve trajectory prediction accuracy and efficiency.
