Autonomous Navigation Map Data Eviction Using Priority Scoring
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Solution Overview
Problem
Conventional approaches for managing map data in vehicular navigation systems inefficiently use memory, leading to unnecessary resource overhead and latency due to the eviction of map data likely to be needed in the near future.
Innovation Solution
Computing priority scores for map data units based on attributes such as recency of use, frequency, distance from current location, and overlap with known routes to intelligently prioritize evictions, reducing the need for subsequent redownloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional LRU or first-available approaches are used to evict map data, then memory storage capacity is maintained, but map data likely to be needed in the near future is deleted causing additional resource overhead and latency
Solution Approach 1:
The patent changes the eviction parameter from simple recency-based metrics to a composite priority score that incorporates multiple attributes including recency of use, frequency of use, distance from current location, and overlap with known routes. This multi-parameter approach allows the system to distinguish between map data that is temporarily unused versus data that is unlikely to be needed soon, thereby reducing unnecessary evictions and subsequent redownloads.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring usage patterns, route planning information, and geographic data to dynamically adjust eviction priorities. The priority scores are recalculated based on updated attributes, allowing the system to learn from past behavior and improve eviction decisions over time, thus minimizing redownloads while maintaining memory capacity.
2Quantity of substance
If conventional LRU or first-available approaches are used to evict map data, then memory storage capacity is maintained, but resource overhead increases due to unnecessary redownloads
Solution Approach 1:
The patent transforms the eviction decision from a single-parameter LRU metric to a multi-attribute priority scoring system that evaluates recency of use, frequency of use, distance from current location, and route overlap. This comprehensive parameter set enables more accurate prediction of future data needs, reducing unnecessary redownloads and associated resource overhead while preserving essential map data in memory.
Solution Approach 2:
The system performs preliminary evaluation of map data units by calculating priority scores based on multiple attributes before eviction decisions are made. This advance assessment allows the system to identify and protect data that is likely to be needed soon, preventing premature evictions that would trigger costly redownloads and reduce overall resource efficiency.
3Productivity
If intelligent priority scoring based on multiple attributes is implemented, then data reuse is improved and redownloads are reduced, but computational complexity increases
Solution Approach 1:
The patent segments the map data storage into discrete map data units or tiles that can be independently evaluated and evicted. Each unit is assigned a priority score based on its specific attributes, allowing the system to manage complexity by processing individual units rather than evaluating the entire map dataset at once. This segmentation enables efficient computation while maintaining high data reuse through intelligent eviction decisions.
4Quantity of substance
If map data is evicted from memory to make room for new data, then storage capacity is optimized, but network utilization increases due to redownloads
Solution Approach 1:
The patent implements parameter changes by transitioning from simple LRU eviction to a multi-attribute priority scoring system that evaluates recency of use, frequency of use, distance from current location, and route overlap. This approach optimizes storage capacity utilization by making more accurate eviction decisions, thereby reducing network traffic from unnecessary redownloads while maintaining efficient memory usage.
Data Source
AI summary
In various examples, a technique for managing map data stored in a location-aware system includes determining a corresponding set of attributes for each of a plurality of map data units stored in a memory within the location-aware system. For each map data unit included in the plurality of map data units, the technique computes a priority score for the map data unit based on the set of attributes corresponding to the map data unit. The technique also includes determining, based on the plurality of priority scores for the plurality of map data units, one or more map data units to be evicted from the memory. The technique further includes causing at least a portion of the one or more map data units to be deleted from the memory in response to receiving one or more new map data units for storage in the memory.