Avoidance Area Ranking for Autonomous Vehicle Route Remapping
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Solution Overview
Problem
Large-scale mapping operations face challenges in efficiently remapping avoidance areas for autonomous vehicles due to the rapid establishment of these areas, leading to suboptimal routing that can increase costs and inconvenience, as existing statistical approaches are computationally cumbersome and impractical.
Innovation Solution
A computing system that identifies and ranks avoidance areas based on their network effects on routing metrics, grouping them by proximity and overlap to prioritize resource allocation for remapping, using a recursive influence evaluation algorithm like PageRank to determine the most impactful areas to resolve first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If avoidance areas are established rapidly to ensure safety, then routing safety is improved, but routing efficiency deteriorates due to suboptimal routes
Solution Approach 1:
The system performs preliminary actions by proactively identifying and remapping avoidance areas before they significantly impact routing efficiency. By using the ranking system to predict which avoidance areas will have the most negative impact on routing, the system addresses safety concerns in advance while preventing efficiency degradation, rather than waiting for problems to manifest.
2Productivity
If remapping operations are performed frequently to resolve avoidance areas, then routing efficiency is improved, but resource consumption increases
Solution Approach 1:
The system applies local quality by focusing remapping resources on specific high-impact avoidance areas rather than uniformly remapping all areas. The ranking system identifies which avoidance areas have the most significant negative impact on routing, allowing resources to be concentrated on those specific locations where remapping will yield the greatest efficiency improvement.
Solution Approach 2:
The system changes the parameter of resource allocation from uniform distribution to priority-based distribution. By introducing a ranking parameter that quantifies the impact of each avoidance area on routing efficiency, the system transforms resource allocation into a targeted operation that optimizes the ratio of routing efficiency improvement to resource consumption.
3Measurement precision
If statistical approaches are used to prioritize avoidance area remapping, then remapping priority accuracy is improved, but computational complexity increases
Solution Approach 1:
The system introduces an intermediary mechanism - a ranking system that uses simulated routing operations to evaluate avoidance area impact. This intermediary translates complex routing network effects into simple rank values, providing accurate prioritization without requiring direct computation of all possible routing scenarios. The intermediary layer simplifies the decision-making process while maintaining measurement precision.
Data Source
AI summary
A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.