Active Probing with Multimodal Predictions for Motion Planning

2025-07-13

AI Summary

To address decision-making risks arising from uncertainty in surrounding agents’ behaviors within dynamic traffic environments, this paper proposes a risk-sensitive motion planning framework integrating multimodal trajectory prediction with active probing. Methodologically: (1) we design a novel analytically tractable risk metric, yielding closed-form solutions and finite-value guarantees under Gaussian mixture trajectory predictions; (2) we introduce an information-gain-driven active probing mechanism to dynamically reduce ambiguity in estimating other agents’ intent parameters; (3) we formulate a unified probabilistic planning objective that jointly optimizes trajectory generation and probing strategies. Evaluated on the large-scale MetaDrive simulator, our approach significantly improves navigation safety and decision robustness in complex interactive scenarios while maintaining compatibility with diverse traffic participant behavior models.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Multiagent Systems: Multiagent Planning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that combines trajectory planning with multimodal predictions and active probing to enhance decision-making under uncertainty. We develop a novel risk metric that seamlessly integrates multimodal prediction uncertainties through mixture models. When these uncertainties follow a Gaussian mixture distribution, we prove that our risk metric admits a closed-form solution, and is always finite, thus ensuring analytical tractability. To reduce prediction ambiguity, we incorporate an active probing mechanism that strategically selects actions to improve its estimates of behavioral parameters of other agents, while simultaneously handling multimodal uncertainties. We extensively evaluate our framework in autonomous navigation scenarios using the MetaDrive simulation environment. Results demonstrate that our active probing approach successfully navigates complex traffic scenarios with uncertain predictions. Additionally, our framework shows robust performance across diverse traffic agent behavior models, indicating its broad applicability to real-world autonomous navigation challenges. Code and videos are available at https://darshangm.github.io/papers/active-probing-multimodal-predictions/.

Problem

• Enhance decision-making under uncertainty in dynamic environments
• Integrate multimodal prediction uncertainties with novel risk metric
• Reduce prediction ambiguity via strategic active probing actions

Innovation

• Combines trajectory planning with multimodal predictions
• Develops a novel risk metric using mixture models
• Incorporates active probing to reduce prediction ambiguity