Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments

2025-04-01

AI Summary

To address excessive conservatism or motion stagnation arising from the decoupling of conformal prediction (CP) and control in dynamic, densely cluttered obstacle environments, this paper proposes a safety-driven, ego-centric CP-based navigation framework. Methodologically, we introduce the first ego-centric non-conformity score function that responds exclusively to safety-critical prediction errors; embed CP within a model predictive control (MPC) closed loop to enable error-sensitive, adaptive safety decision-making; and integrate dynamic obstacle modeling with confidence-adaptive calibration. Theoretically, we prove that our framework achieves superior cost efficiency compared to existing CP-based approaches. Empirically, on high-density pedestrian scenarios, it attains a safety coverage rate of ≥99.7% while maintaining real-time navigation efficiency.

Technology Category

• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Conformal prediction (CP) has emerged as a powerful tool in robotics and control, thanks to its ability to calibrate complex, data-driven models with formal guarantees. However, in robot navigation tasks, existing CP-based methods often decouple prediction from control, evaluating models without considering whether prediction errors actually compromise safety. Consequently, ego-vehicles may become overly conservative or even immobilized when all potential trajectories appear infeasible. To address this issue, we propose a novel CP-based navigation framework that responds exclusively to safety-critical prediction errors. Our approach introduces egocentric score functions that quantify how much closer obstacles are to a candidate vehicle position than anticipated. These score functions are then integrated into a model predictive control scheme, wherein each candidate state is individually evaluated for safety. Combined with an adaptive CP mechanism, our framework dynamically adjusts to changes in obstacle motion without resorting to unnecessary conservatism. Theoretical analyses indicate that our method outperforms existing CP-based approaches in terms of cost-efficiency while maintaining the desired safety levels, as further validated through experiments on real-world datasets featuring densely populated pedestrian environments.

Problem

• Ensures safe robot navigation in dynamic cluttered environments
• Reduces conservatism by focusing on safety-critical prediction errors
• Integrates egocentric scoring with model predictive control

Innovation

• Egocentric score functions for safety-critical errors
• Model predictive control with individual safety evaluation
• Adaptive conformal prediction for dynamic obstacle adjustment