Transition Variability Index for Autonomous Driving Data Prioritization
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Solution Overview
Problem
Autonomous driving technologies face challenges in accurately transitioning between different levels of autonomy due to varying environmental and vehicle conditions, leading to inconsistencies in vehicle navigation and control.
Innovation Solution
A method and apparatus that calculate a transition variability index for spatial reference points along road segments, allowing for the prioritization of updating transition data based on the comparison of variability indices between different points, thereby enhancing autonomous driving precision and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transition data is updated uniformly across all spatial reference points, then data consistency is maintained, but computational resources are wasted on low-variability regions
Solution Approach 1:
The patent applies local quality by differentiating update frequencies based on spatial location characteristics. The system calculates variability indices for different spatial reference points and assigns update priorities accordingly - high-variability regions receive frequent updates while low-variability regions receive less frequent updates, optimizing resource allocation according to local needs
Solution Approach 2:
The system changes the parameter of update frequency based on the calculated variability index. By dynamically adjusting the update interval parameter according to the variability characteristics of each spatial reference point, the system achieves both data consistency where needed and resource efficiency where variability is low
2Measurement precision
If transition data is updated frequently for all spatial reference points, then navigation precision is improved, but system complexity increases
Solution Approach 1:
The patent introduces a variability index parameter that quantifies the degree of transition variability at each spatial reference point. This parameter enables the system to differentiate update needs and adjust update frequencies accordingly, achieving high navigation precision only where variability demands it rather than uniformly across all regions
Solution Approach 2:
The system segments the operational space into multiple spatial reference points with different variability characteristics. By calculating and comparing variability indices across these segments, the system applies differentiated update strategies, reducing overall system complexity while maintaining precision where critical
3Productivity
If variability-based prioritization is implemented, then resource efficiency is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent introduces a variability index as an intermediary metric that simplifies the measurement of transition variability. This index serves as a mediator between the complex underlying variability phenomena and the resource allocation decisions, making the measurement and detection process more manageable while still enabling effective prioritization
Data Source
AI summary
A method, apparatus and computer program product are provided for generating a transition variability index related to autonomous driving. In this regard, a first transition variability index is calculated. The first transition variability index is indicative of a first degree of variability in relation to transition of vehicles from respective autonomous levels while traveling proximate a first spatial reference point. Furthermore, a second transition variability index is generated. The second transition variability index is indicative of a second degree of variability in relation to transition of vehicles from respective autonomous levels while traveling along proximate a second spatial reference point. Updating of transition data associated with one of the first and second spatial reference points relative to another one of the first and second spatial reference points is then prioritized based on a comparison between the first transition variability index and the second transition variability index.