Neural Network Reference Map for Dynamic Vehicle Localization
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Solution Overview
Problem
Current methods for creating reference maps and localizing motor vehicles in dynamic environments are inefficient and lack robustness in handling varying sensor states and environments, as they rely on complex sensor fusion and data accumulation without focusing on relevant data features.
Innovation Solution
A method utilizing an artificial neural network with an encoder and decoder is trained on point cloud and state data to create a reference map representation, allowing for efficient localization and simulation of point clouds by reconstructing environments without relying on original point cloud data, using state data to enrich and filter information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex sensor fusion and data accumulation methods are used to create reference maps, then the reference map can represent dynamic environments, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The patent extracts and separates the static reference map data from dynamic sensor data. The encoder extracts relevant features from point cloud data to create a compressed reference map representation, while the decoder reconstructs current environment data by combining this extracted reference information with current sensor measurements, thereby handling dynamic environments without requiring complex fusion of all historical data
Solution Approach 2:
The system segments the environment representation into static reference map components (encoded from historical data) and dynamic current state components (from real-time sensors). This segmentation allows independent processing of static and dynamic elements, reducing overall system complexity while maintaining adaptability to environmental changes
2Loss of information
If all point cloud data is used for reference map creation, then complete environmental information is captured, but data processing time and computational resources increase
Solution Approach 1:
The patent transforms the point cloud data from its original high-dimensional form into a compressed latent space representation through the encoder. This parameter transformation reduces data dimensionality while preserving essential environmental features, enabling faster processing without significant information loss. The decoder then reconstructs the necessary information from this compressed representation combined with current sensor data
3Measurement precision
If traditional localization methods are used, then localization can be performed, but accuracy decreases in dynamic environments with varying sensor states
Solution Approach 1:
The system implements a feedback mechanism where the decoder uses current sensor state information to adjust the reconstruction of environmental data. By comparing reconstructed data with actual current sensor measurements, the system can refine localization estimates and adapt to varying sensor states, thereby improving accuracy in dynamic environments
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the decoder to adjust its reconstruction process based on current sensor states. The system dynamically weights and combines reference map information with current sensor data according to the actual sensor conditions, enabling accurate localization even when sensor states vary due to environmental factors or sensor limitations
Data Source
AI summary
A method for creating a reference map representation using an artificial neural network, wherein the neural network comprises an encoder and a decoder. The method includes: receiving point cloud data representing a point cloud of the environment of the motor vehicle that was created on the basis of a detection of an environment of a motor vehicle by an environmental sensor of the motor vehicle; receiving state data representing a state of the environmental sensor during the detection of the environment by the environmental sensor; training the encoder and the decoder on the basis of the point cloud data and the state data.