Motion Forecasting via Model-Based Risk Minimization

Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its application in trajectory prediction is limited due to the multi-modal nature of predictions. In this paper, we propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models. We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models. To address this problem, we introduce a new method that generates optimal trajectories from a set of neural networks, framing it as a risk minimization problem with a variable loss function. By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling. Extensive experiments on the nuScenes prediction dataset demonstrate that our method surpasses current state-of-the-art techniques, achieving top ranks on the leaderboard. We also provide a comprehensive empirical study on ensembling strategies, offering insights into their effectiveness. Our findings highlight the potential of advanced ensembling techniques in trajectory prediction, significantly improving predictive performance and paving the way for more reliable predicted trajectories.

Authors

• Aron Distelzweig
• Eitan Kosman
• Andreas Look
• Faris Janjos
• Denesh K. Manivannan
• Abhinav Valada

Keywords

• Risk minimization
• Accuracy
• Predictive models
• Sampling methods
• Trajectory
• Safety
• Planning
• Reliability
• Forecasting
• Robotics and automation
• Loss Function
• Neural Network
• Sampling Method
• Ensemble Model
• Optimal Sample
• Base Learners
• Set Of Networks
• Improve Prediction Accuracy
• Trajectory Optimization
• Future Trajectories
• Prediction Techniques
• Trajectory Prediction
• Ensemble Strategy
• Learning Models
• Convolutional Neural Network
• Rich Information
• Limitations Of Approaches
• Individual Models
• Generative Adversarial Networks
• Gaussian Mixture Model
• Prediction Set
• Non-maximum Suppression
• Graph Neural Networks
• Number Of Proposals
• Number Of Trajectories
• Heterogeneous Model
• Sample Trajectories
• Prior Assumptions
• Ensemble Performance
• Distribution Of Categories