Autonomous Vehicle Localization Using Sparse Geographic Cues
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Solution Overview
Problem
Autonomous vehicles face challenges in determining their location within unfamiliar or modified environments with minimal computational resources, relying on detailed maps being unreliable and resource-intensive.
Innovation Solution
A computationally lightweight localization system for autonomous vehicles that uses sparse geographic data, such as lane graphs, to determine location by comparing sensor data with transformed geographic cues, employing machine-learned models for efficient matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed maps are used for autonomous vehicle localization, then localization accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts only the essential geographic cues (lane boundaries, road edges, intersections) from the environment to create sparse geographic data, eliminating unnecessary detailed map information. This extraction approach maintains localization accuracy by focusing on key features while significantly reducing computational resource consumption for processing and storage.
Solution Approach 2:
The patent applies local quality by creating sparse geographic data with varying levels of detail - dense representation of critical localization features (lane boundaries, intersections) and sparse representation of less important areas. This allows the system to maintain high localization accuracy where needed while reducing overall computational burden.
2Measurement precision
If detailed maps are used for autonomous vehicle localization, then localization accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts only essential geographic features (lane boundaries, road edges, intersections) from the complex environment, creating simplified sparse geographic data. This extraction eliminates unnecessary map details and system complexity while preserving the key information needed for accurate localization.
Solution Approach 2:
The patent segments the localization system into distinct modules: sensor data acquisition, sparse geographic data generation, cue extraction, and localization computation. This segmentation simplifies the overall system by breaking down complex operations into manageable, independent components that can be processed efficiently.
3Speed
If real-time localization is achieved, then navigation responsiveness is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-generating sparse geographic data from sensor inputs and pre-extracting geographic cues before the actual localization computation. This preliminary processing organizes data in advance, enabling faster real-time localization while reducing the computational burden during critical navigation moments.
Solution Approach 2:
The system uses partial action by processing only the essential geographic cues needed for localization rather than analyzing all sensor data. By focusing computation on relevant features (lane boundaries, intersections) rather than exhaustive scene understanding, the system achieves real-time performance with reduced computational resource consumption.
Data Source
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AI summary
Systems and methods for autonomous vehicle localization are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system that includes one or more computing devices onboard an autonomous vehicle, sensor data indicative of one or more geographic cues within the surrounding environment of the autonomous vehicle. The method includes obtaining, by the computing system, sparse geographic data associated with the surrounding environment of the autonomous vehicle. The sparse geographic data is indicative of the one or more geographic cues. The method includes determining, by the computing system, a location of the autonomous vehicle within the surrounding environment based at least in part on the sensor data indicative of the one or more geographic cues and the sparse geographic data. The method includes outputting, by the computing system, data indicative of the location of the autonomous vehicle within the surrounding environment.