Fully Coupled Crowd Navigation Models to Prevent Freezing Robots
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Solution Overview
Problem
Current crowd navigation systems face challenges in efficiently navigating a host vehicle through congested environments due to the 'freezing robot problem' (FRP), where the host vehicle freezes or takes unnecessary evasive maneuvers, leading to decreased efficiency and safety, especially at high crowd densities, and there is a lack of understanding in formulating effective collision avoidance functions.
Innovation Solution
A system and method utilizing fully coupled models of first and second-order interactions, incorporating Gaussian Processes and Gaussian Mixture Models to generate a convex configuration of agents in a physical environment, optimizing collision avoidance and improving safety and efficiency by considering both host-agent and agent-agent interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If independently modeling a host for crowd navigation, then the model complexity is reduced, but the host freezes or takes unnecessary evasive maneuvers causing decreased efficiency and safety
Solution Approach 1:
The patent merges the host model with agent models into a unified fully coupled model. Instead of independently modeling the host and agents, the system creates a joint probability distribution that captures both first-order (host-agent) and second-order (agent-agent) interactions simultaneously. This integration eliminates the freezing robot problem by allowing the host to observe and react to agent-agent interactions, thereby improving navigation reliability without excessive complexity.
Solution Approach 2:
The fully coupled model implements feedback mechanisms where the host observes agent-agent interactions and adjusts its behavior accordingly. The joint probability distribution continuously updates based on observed agent behaviors and interactions, enabling the host to learn from the crowd dynamics and make more accurate navigation decisions, thus improving reliability.
2Reliability
If considering both first-order and second-order interactions in fully coupled models, then collision avoidance performance is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the interaction model into distinct first-order (host-agent) and second-order (agent-agent) components. By separating these interaction types, the system can efficiently compute each component independently and then combine them in the joint probability distribution. This segmentation allows the model to capture complex interactions while managing computational complexity through modular computation.
Solution Approach 2:
The system uses Gaussian process parameters to represent and compute the joint probability distribution. By changing the parameterization approach to use Gaussian processes with specific covariance functions, the computationally intensive fully coupled model becomes tractable. The Gaussian process parameters efficiently capture the spatial and temporal correlations in agent behaviors, reducing computational complexity while maintaining collision avoidance performance.
3Use of energy by moving object
If using traditional independent host modeling, then computational resources are saved, but safety and efficiency decrease at high crowd densities
Solution Approach 1:
The system performs preliminary computation by pre-defining the Gaussian process models and their covariance structures for representing agent behaviors. This preliminary action allows the fully coupled model to be efficiently evaluated during runtime without requiring excessive computational resources. The pre-computed Gaussian process parameters enable rapid inference of the joint probability distribution, maintaining navigation efficiency even at high crowd densities.
Data Source
AI summary
Systems and methods for utilizing interactive Gaussian processes for crowd navigation are provided. In one embodiment, a system for a crowd navigation includes a processor, a statistical module, and a model module. The processor receives sensor data. The statistical module identifies a number of agents in a physical environment based on the sensor data. The statistical module further calculates a set of Gaussian processes. The set of Gaussian processes includes a Gaussian Process for each agent of the number of agents. The statistical module further determines an objective function based on an intent and a flexibility for the host and at least two agent of the plurality of agents. The model module generates a model of the number of agents by applying the objective function to the set of Gaussian processes. The model includes a convex configuration of the number of agents in the physical environment.