Multi-Agent Vehicle Path Planning With Nash Equilibrium Calibration
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Solution Overview
Problem
Current vehicle path planning technologies face challenges in accurately predicting the actions of multiple agents, including autonomous and non-autonomous vehicles, pedestrians, and stationary objects, in real-time, especially in dynamic traffic environments, due to limitations in data processing and computational resources.
Innovation Solution
The implementation of a modified Nash equilibrium solution using an adaptive grid search optimization technique, which calibrates utility functions based on real-world data to determine optimal vehicle actions by simulating the behavior of multiple agents and minimizing a cost function, allowing for real-time decision-making at sub-second frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path planning methods are used to predict actions of multiple agents, then computational resources and data processing requirements increase, but path planning accuracy decreases in real-time dynamic traffic environments
Solution Approach 1:
The patent transforms the path planning problem from predicting continuous agent actions to classifying discrete path segments. By changing the parameter space from continuous action predictions to discrete path segment classifications, the system achieves real-time processing with reduced computational resources while maintaining accuracy in dynamic traffic environments
Solution Approach 2:
The patent divides the road network into discrete path segments and formulates path planning as a sequence of segment selections. This segmentation approach breaks down the complex continuous control problem into manageable discrete decisions, enabling efficient real-time computation while accurately predicting vehicle paths among multiple agents
2Speed
If real-time data processing is performed at sub-second frequency to determine optimal vehicle actions, then response time improves, but computational load increases
Solution Approach 1:
The patent implements a dynamic path planning system that updates vehicle paths at sub-second frequencies by formulating the problem as a sequence of discrete segment classifications. This dynamic approach enables real-time adaptation to changing traffic conditions while using efficient classification algorithms that reduce computational energy consumption compared to continuous optimization methods
3Measurement precision
If utility functions are calibrated using extensive real-world data to simulate multiple agent behaviors, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The patent performs preliminary calibration of utility functions using real-world data to establish accurate agent behavior models before real-time operation. By pre-calibrating the classification parameters and path segment utilities offline, the system achieves high prediction accuracy during real-time execution without incurring calibration delays, separating the data-intensive calibration phase from the real-time decision phase
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to calibrate utility functions that determine optimal vehicle actions based on an approximate Nash equilibrium solution for multiple agents by determining a difference between model-predicted future states for the multiple agents to observed states for the multiple agents. The instructions can include further instructions to determine a vehicle path for a vehicle based on the optimal vehicle actions.