Vehicle Self-Localization Using Object-to-Map Matching
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Solution Overview
Problem
Traditional methods for road geometry modeling and object detection in autonomous vehicle navigation are resource-intensive and time-consuming, making them inefficient for real-time localization in environments without satellite navigation or with low accuracy, which is critical for autonomous vehicle control.
Innovation Solution
A modularized localization framework that uses on-board sensors like LiDAR and HD maps to identify objects and determine vehicle location with high precision by dynamically allocating resources based on sensor data and ground truth comparisons, reducing the search space and latency in localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods for road geometry modeling and object detection are used, then localization accuracy can be achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the localization process into distinct modules: sensor data acquisition, object detection, feature extraction, and localization calculation. Each module processes specific aspects independently, allowing parallel computation and reducing overall processing time while maintaining localization accuracy through systematic feature analysis
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data to identify and extract key features before the actual localization computation. Objects and environmental features are detected and categorized in advance, creating a prepared dataset that accelerates the final localization determination without sacrificing precision
2Measurement precision
If comprehensive object detection and environment modeling are performed, then localization accuracy improves, but computational resources are consumed excessively
Solution Approach 1:
The patent extracts only the essential features and objects relevant to localization from the complete sensor data stream. By identifying and isolating key environmental features needed for positioning, the system achieves accurate localization while discarding redundant information, thereby reducing computational resource consumption significantly
Solution Approach 2:
The system applies local quality by processing different regions of sensor data with varying levels of detail based on their importance for localization. Critical areas receive intensive processing while less relevant regions are processed more efficiently, optimizing the balance between accuracy and resource usage in the localization pipeline
3Measurement precision
If traditional vision-based map localization is used, then accurate position recognition can be achieved, but latency increases which is problematic for real-time autonomous vehicle control
Solution Approach 1:
The patent implements a dynamic localization approach that adapts processing intensity and search depth based on current vehicle speed, environmental complexity, and confidence levels. This dynamic adjustment allows the system to maintain high accuracy when needed while operating more efficiently during routine conditions, thereby improving overall localization speed without sacrificing position recognition accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate vehicle localization to a centimeter-level in real-time, improving the robustness and efficiency of autonomous vehicle control by reducing processing latency and optimizing resource allocation.
Implementation Method 1
receive sensor data from a vehicle traveling along a road segment
Data Source
AI summary
Methods described herein relate to self-localization. Methods may include: receiving sensor data from a vehicle traveling along a road segment; identifying one or more objects of the environment from the sensor data; determining a coarse location of the vehicle; comparing the identified one or more objects with corresponding ground truth objects; calculating an observation in response to the comparison of the identified one or more objects with the corresponding ground truth objects; determining a location of the vehicle with a higher precision than the coarse location based, at least in part, on the observation; and allocating resources of the apparatus based, at least in part, on the observation.