Adaptive Map Data Segmentation for Vehicle Localization
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Solution Overview
Problem
Autonomous driving systems require large computational resources to achieve high accuracy and precision in vehicle localization, which increases complexity and cost, but not all environments necessitate such high levels of precision, leading to inefficient resource utilization.
Innovation Solution
A system that determines accuracy specifications for different types of roads and creates or refines map data to include only the necessary features for localization, allowing the vehicle to transition between different map data sets based on the current road type, reducing the number of features and sensor inputs required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of sensor input data, map data, and other datasets are processed to achieve high accuracy and precision in vehicle localization, then localization accuracy is improved, but computational resource consumption and system complexity increase
Solution Approach 1:
The patent segments map data into multiple versions with different levels of detail and feature sets. A first version of map data contains comprehensive features for high-precision localization, while a second version contains reduced feature sets for lower-precision scenarios. The system selectively processes different map data versions based on environmental context and localization requirements, thereby reducing computational complexity while maintaining necessary accuracy.
Solution Approach 2:
The patent applies local quality by providing different quality levels of map data for different geographic locations or road types. Instead of uniformly processing high-detail map data everywhere, the system adjusts the detail and feature richness of map data locally based on specific location characteristics, such as urban versus rural areas, or high-traffic versus low-traffic zones.
2Measurement precision
If large amounts of sensor input data, map data, and other datasets are processed to achieve high accuracy and precision in vehicle localization, then localization accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements partial action by processing only the necessary subset of map data features required for adequate localization in given conditions. Instead of always processing all available data, the system selectively processes a partial set of features from map data based on current localization needs, environmental conditions, and required precision levels, thereby reducing computational resource consumption while maintaining sufficient accuracy.
3Reliability
If comprehensive map data with all features is used for vehicle localization in all environments, then localization accuracy is maintained across all conditions, but system efficiency decreases
Solution Approach 1:
The patent applies dynamics by making the map data processing configuration adaptive and changeable based on real-time conditions. The system dynamically selects which version of map data to use and what level of feature processing to apply, adjusting to environmental factors, vehicle speed, road type, and localization requirements. This dynamic approach maintains reliability across varying conditions while optimizing system efficiency for each specific scenario.
Data Source
AI summary
A system for localizing a vehicle in an environment having a computing device comprising a processor and a non-transitory computer readable memory, a first map data stored in the non-transitory computer readable memory, where the first map data defines features within an environment used to localize a vehicle within the environment, and a machine-readable instruction set. The machine-readable instruction set causes the computing device to: determine a portion of the first map data having a first type of road, determine a first accuracy specification for the first type of road, wherein the first accuracy specification identifies one or more features of the plurality of features defined in the first map data used to localize a vehicle traversing the first type of road within a predefined degree of accuracy, and create a second map data for the first type of road.