HD Map Reverse Rendering for Autonomous Vehicle Localization
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Solution Overview
Problem
Autonomous vehicles face challenges in accurate navigation due to limitations in conventional map data accuracy and sensor data reliability, leading to potential errors in localization and decision-making, which can result in unsafe driving conditions.
Innovation Solution
A system that combines sensor data from autonomous vehicles with high-definition map data, compensating for vehicle motion and overlaying sensor data onto map data to visualize the vehicle's surroundings, allowing for accurate localization and real-time updates, and performs corrective actions when errors exceed a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional maps are used for autonomous vehicle navigation, then the system complexity is reduced, but the localization accuracy deteriorates to over 100 meters
Solution Approach 1:
The patent combines multiple data sources including sensor data from autonomous vehicles, high-definition map data, and motion compensation data to create an integrated localization system. This merging of multiple information streams achieves centimeter-level accuracy that cannot be obtained from conventional maps alone, while distributing system complexity across multiple components rather than concentrating it in a single system.
2Measurement precision
If high definition map data is used to improve localization accuracy, then the navigation precision is improved to 10 cm or less, but the device complexity increases
Solution Approach 1:
The patent segments the localization system into distinct functional modules: sensor data acquisition, high-definition map data processing, motion compensation, and integration layers. This segmentation allows each component to be optimized independently and managed separately, reducing the perceived complexity while achieving high precision localization through coordinated operation of specialized subsystems.
Solution Approach 2:
The patent introduces motion compensation as an intermediary element that bridges sensor data and high-definition map data. This intermediary component corrects for vehicle motion between data capture and processing, enabling accurate alignment of sensor observations with map features and achieving centimeter-level localization accuracy without requiring overly complex direct processing of all raw data.
3Speed
If sensor data is used for real-time navigation decisions, then the responsiveness is improved, but the reliability deteriorates due to obstructions and limited observation
Solution Approach 1:
The patent implements feedback mechanisms where sensor data is continuously compared against high-definition map data to verify localization accuracy. This feedback loop allows the system to detect discrepancies between expected and observed sensor readings, identify potential sensor failures or obstructions, and correct localization estimates using map-based references, thereby maintaining reliability while preserving real-time responsiveness.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing high-definition map data in advance, and by using motion compensation to predict and correct sensor data before final processing. This preliminary preparation of reference data and motion correction allows the system to quickly compare current sensor readings against pre-prepared expectations, enabling fast reliable decisions even when sensors are temporarily obscured or fail.
4Productivity
If the vehicle moves between sensor data capture and processing, then real-time navigation is enabled, but the localization accuracy deteriorates due to location changes
Solution Approach 1:
The patent uses motion compensation as an intermediary transformation that accounts for vehicle movement between sensor data capture and processing. This intermediary computation applies correction factors based on measured vehicle motion to align the sensor data with the vehicle's current position in the high-definition map, enabling real-time navigation while maintaining centimeter-level localization accuracy despite continuous vehicle movement.
Data Source
AI summary
The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.