Dynamic Map Annotation of Drivable Areas for Vehicle Localization
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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, such as drivable areas, which are then embedded into a live map, allowing for real-time updates and improved navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static maps are used for navigation, then the system complexity is low, but the accuracy and reliability of localization deteriorates due to inability to capture 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, moving obstacles, and dynamic traffic patterns, allowing the map to adapt to current conditions rather than relying on outdated static information.
Solution Approach 2:
The patent embeds multiple levels of detail within the map structure by nesting geometric models (3D representations) within semantic annotations, which are themselves nested within the broader map context. This hierarchical nesting allows the system to maintain comprehensive information while managing complexity through organized layers of detail.
2Adaptability or versatility
If traditional mapping methods are used, then the ease of manufacture is high, but the adaptability to large environments and changing conditions deteriorates
Solution Approach 1:
The patent creates a universal mapping system that can handle multiple types of environments (urban, rural, indoor, outdoor) and various changing conditions (weather, traffic, temporary obstacles) through a single integrated approach. The system uses standardized geometric models and semantic annotations that can represent diverse environmental features uniformly.
Solution Approach 2:
The patent performs preliminary processing of sensor data by generating geometric models and extracting semantic annotations in advance before they need to be used for navigation decisions. This preprocessing approach allows the system to have ready-to-use structured information when making real-time navigation choices.
3Loss of information
If static maps without real-time updates are used, then the loss of time for map updates is minimal, but the loss of information about dynamic road conditions increases
Solution Approach 1:
The patent implements continuous map updating by constantly processing sensor data and refreshing the dynamic map with current environmental information. Rather than periodic updates, the system maintains continuous awareness of changing conditions, ensuring navigation decisions are always based on the most recent data.
Solution Approach 2:
The patent extracts specific useful information from raw sensor data by identifying and isolating relevant features such as drivable areas, obstacles, and environmental characteristics. This extraction process filters out unnecessary data while capturing essential dynamic conditions needed for safe and efficient navigation.
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 precise localization and navigation by dynamically updating maps with changing conditions, enhancing safety and efficiency by providing accurate and real-time information to autonomous 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.