Dynamic Map Annotation for Autonomous Vehicle Navigation
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Solution Overview
Problem
Conventional navigation methods for autonomous vehicles rely on static maps that fail to accurately account for changing environmental features and dynamic road conditions, particularly in large environments like cities or states, leading to inefficiencies and safety concerns.
Innovation Solution
The implementation of a system that uses sensors like LiDAR and cameras to generate and annotate geometric models of environmental features within a map, allowing for real-time updates and semantic data integration, enabling precise localization and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional static maps are used for navigation, then the system is simple and easy to operate, but the navigation accuracy and safety deteriorate due to inability to account for changing environmental features
Solution Approach 1:
The patent transforms static maps into dynamic maps that are continuously updated with real-time sensor data. The system captures changing environmental features such as temporary road conditions, construction zones, and dynamic obstacles, allowing the navigation system to adapt to current conditions rather than relying on outdated static information.
Solution Approach 2:
The patent segments the environment into discrete geometric blocks that can be independently processed and updated. Each block represents a specific spatial region with particular features, allowing the system to efficiently manage complex environmental data by dividing it into manageable units that can be individually annotated and integrated into the map.
2Adaptability or versatility
If traditional mapping methods are used, then the device complexity is low, but the scalability to large environments such as cities or states deteriorates
Solution Approach 1:
The patent divides large environments into a grid of geometric blocks, where each block is independently processed and annotated. This segmentation allows the system to scale to city or state-level environments by breaking down vast spaces into manageable units that can be processed sequentially or in parallel, rather than attempting to map entire regions as single complex structures.
Solution Approach 2:
The patent introduces a hierarchical dimension to the mapping system by organizing geometric blocks into larger spatial structures. The system operates at multiple scales, from individual blocks to neighborhoods to entire cities, allowing efficient navigation in large environments by navigating through hierarchical levels rather than processing all details at once.
3Adaptability or versatility
If static maps are used for navigation, then the system is simple to operate, but the ability to account for dynamic road conditions and changing environmental features deteriorates
Solution Approach 1:
The patent implements continuous feedback loops where sensor data from the vehicle is constantly compared against the map, and discrepancies trigger automatic map updates. The system monitors environmental changes in real-time and feeds this information back into the mapping system, ensuring the map remains current with dynamic conditions such as new construction zones, temporary road closures, or changing traffic patterns.
Solution Approach 2:
The patent transforms the static map into a dynamic structure that evolves continuously based on incoming sensor data. Rather than requiring complete remapping of environments, the system dynamically updates specific geometric blocks as conditions change, allowing the map to adapt to dynamic road conditions and environmental features in real-time.
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 enhances navigation accuracy and safety by providing a dynamic, real-time map that accounts for changing conditions, improving passenger and pedestrian safety while reducing travel time and wear on vehicles.
Implementation Method 1
the sensor data includes LiDAR point cloud data
Implementation Method 2
the one or more sensors includes a camera and the sensor data further includes an image of the plurality of features of the environment
Data Source
AI summary
Among other things, we describe techniques for automatic annotation of environmental features in a map during navigation of a vehicle. The techniques include receiving, by the vehicle located within an environment, a map of the environment. Sensors of the vehicle receive sensor data and semantic data. The sensor data includes a plurality of features of the environment. A geometric model of a feature of the plurality of features is generated. The feature is associated with a drivable area within the environment. A drivable segment is extracted from the drivable area. The drivable segment is segregated into a plurality of geometric blocks, wherein each geometric block corresponds to a characteristic of the drivable area and the geometric model of the feature includes the plurality of geometric blocks. The geometric model is annotated using the semantic data. The annotated geometric model is embedded within the map.