Autonomous Vehicle Agent Prioritization by Interaction Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face inefficiencies in computational power and reaction time due to existing methods and systems for determining trajectories, leading to potential real-world complications.
Innovation Solution
Implementing a method for agent prioritization using interaction parameters to filter and prioritize agents in the autonomous vehicle's environment, allowing for reduced computational times and modified computational fidelity by applying high-fidelity techniques only to important agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity trajectory determination methods are applied to all agents, then measurement precision is improved, but computational power consumption increases and reaction time slows
Solution Approach 1:
The patent segments agents into different priority levels (first priority agents and second priority agents) based on their interaction parameters with the autonomous vehicle. This segmentation allows the system to apply different computational fidelity levels to different agent groups, thereby improving overall computational efficiency while maintaining necessary precision for critical agents.
Solution Approach 2:
The patent applies local quality by using high-fidelity trajectory determination methods only for first priority agents (those with higher interaction parameters) and low-fidelity methods for second priority agents (those with lower interaction parameters). This localized application of computational resources optimizes the balance between precision and efficiency.
2Measurement precision
If high-fidelity trajectory determination methods are applied to all agents, then measurement precision is improved, but reaction time increases
Solution Approach 1:
The patent segments agents into different priority levels (first priority agents and second priority agents) based on their interaction parameters with the autonomous vehicle. This segmentation allows the system to apply different computational fidelity levels to different agent groups, thereby improving overall computational efficiency while maintaining necessary precision for critical agents.
Solution Approach 2:
The patent applies local quality by using high-fidelity trajectory determination methods only for first priority agents (those with higher interaction parameters) and low-fidelity methods for second priority agents (those with lower interaction parameters). This localized application of computational resources optimizes the balance between precision and efficiency.
3Measurement precision
If all agents are processed with equal computational fidelity, then measurement precision is maintained, but computational load increases
Solution Approach 1:
The patent segments agents into different priority levels (first priority agents and second priority agents) based on their interaction parameters with the autonomous vehicle. This segmentation allows the system to apply different computational fidelity levels to different agent groups, thereby improving overall computational efficiency while maintaining necessary precision for critical agents.
Solution Approach 2:
The patent applies local quality by using high-fidelity trajectory determination methods only for first priority agents (those with higher interaction parameters) and low-fidelity methods for second priority agents (those with lower interaction parameters). This localized application of computational resources optimizes the balance between precision and efficiency.
Data Source
AI summary
Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.