Autonomous Vehicle Situational Complexity Quantification
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Solution Overview
Problem
Autonomous vehicles face increased computational burdens as scenario complexity grows, affecting their ability to operate effectively, necessitating a method to determine and adapt to situational complexity.
Innovation Solution
A system and method that determine situational complexity by calculating temporal and spatial complexities using sensor data, integrating spatial complexities over time with a temporal kernel that weights recent data more heavily, allowing for adaptive control scheme selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the processor uses more complex processing methods to handle complex driving scenarios, then the operational effectiveness is improved, but the computational burden increases
Solution Approach 1:
The system dynamically adjusts the control scheme based on real-time situational complexity assessment. When complexity is high, more sophisticated processing methods are activated; when complexity is low, simpler methods are used. This dynamic adaptation resolves the contradiction by making computational burden variable rather than fixed, matching processing power to actual needs.
Solution Approach 2:
The patent changes the parameter of processing complexity based on the assessed situational complexity. By introducing a complexity metric that evaluates environmental factors, the system selectively adjusts processing depth and control scheme sophistication, ensuring high operational effectiveness only when computationally justified.
2Productivity
If the processor uses simpler processing methods to reduce computational burden, then the computational efficiency is improved, but the operational effectiveness deteriorates
Solution Approach 1:
The system employs dynamic control scheme selection where processing complexity adapts to situational demands. In simple scenarios, computationally efficient methods are used; in complex scenarios, more sophisticated methods are activated. This resolves the contradiction by making processing complexity variable rather than fixed.
Solution Approach 2:
The patent changes the processing parameter based on situational complexity assessment. By introducing a complexity metric that evaluates environmental factors, the system selectively adjusts processing depth, ensuring computational efficiency is maximized without compromising operational effectiveness when needed.
3Measurement precision
If the system integrates spatial complexities over a long time period, then the temporal accuracy is improved, but the computational burden increases
Solution Approach 1:
The system applies partial integration by using a temporal kernel that weights recent spatial complexities more heavily than distant ones. This partial action approach captures the essential temporal dynamics for accurate complexity assessment without the full computational cost of integrating all historical data equally, resolving the contradiction between temporal accuracy and computational burden.
Data Source
AI summary
A method, autonomous vehicle and system for operating an autonomous vehicle. A sensor obtains data of an agent. A processor determines a measure of complexity of the environment in which the autonomous vehicle is operating from the sensor data, selects a control scheme for operating the autonomous vehicle based on the determined complexity, and operates the autonomous vehicle using the selected control scheme.