Autonomous Vehicle Localization via Self-Generated Detector Modules
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Solution Overview
Problem
Modern transportation vehicles, especially those with partial or full autonomous control, require reliable and accurate self-localization, but existing methods are limited by the need for time-consuming software updates and reliance on specific detectors for environmental feature extraction.
Innovation Solution
A method and system that generate and update detector modules automatically using training data from map and environment information, allowing for comprehensive use of available data to detect landmarks and determine position without requiring frequent software updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific detectors are used for environmental feature extraction, then detection accuracy is improved, but device complexity and software update requirements increase
Solution Approach 1:
The system automatically generates and updates detector modules using machine learning algorithms that process environment data collected by existing sensors. The detectors self-improve over time without requiring external software updates, as the system learns from accumulated data to refine detection capabilities for various landmark types.
Solution Approach 2:
A single unified system performs multiple detection functions across different landmark types (traffic signs, pedestrians, vehicles, etc.) by dynamically generating specialized detector modules. Instead of requiring separate detectors for each feature type, the system uses one multi-functional platform that adapts to detect various environmental features.
2Adaptability or versatility
If detector modules are updated with new landmark types, then adaptability is improved, but loss of time due to software updates increases
Solution Approach 1:
The system automatically generates updated detector modules in real-time using machine learning algorithms that process newly collected environment data. When new landmark types are encountered, the system self-updates by training new detector modules without requiring external software distribution or manual updates, eliminating update time delays.
Solution Approach 2:
The system continuously pre-trains and prepares detector modules in the background using collected environment data. When new landmark types are detected, pre-generated detector modules are already available or can be rapidly generated, eliminating the need for time-consuming software updates during operation.
3Measurement precision
If comprehensive environment data is collected, then position determination accuracy is improved, but use of energy increases
Solution Approach 1:
The system uses a single multi-functional detector that processes environment data for multiple purposes: detecting various landmark types, determining vehicle position, and generating training data for future detections. This unified approach maximizes the utility of collected data while minimizing redundant energy consumption compared to having separate specialized detectors.
Solution Approach 2:
The system efficiently processes environment data by using machine learning algorithms that learn from previously collected data, reducing the computational energy required for each new detection task. The system self-optimizes its processing efficiency over time, maintaining high position determination accuracy while reducing energy consumption through improved algorithmic efficiency.
Data Source
AI summary
A method for providing map data which include position information for first landmarks of a first landmark class, collecting environment data, and determining a position of the mobile unit. Training data are generated and stored for the first landmarks based on the position of the mobile unit, the collected environment data and the position information. Based on the training data, a first detector module is generated for detecting the first landmark class. The position determination system includes a memory unit for providing map data which include position information for first landmarks of a first landmark class, a data acquisition unit for collecting environment data, a localization unit for determining a position of the mobile unit, and a processing unit for generating and storing training data based on the position of the mobile unit, the collected environment data and the position information for the first landmarks.