Road Segment Similarity Mapping for Pre-Navigation Risk Assessment
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Solution Overview
Problem
Conventional approaches for autonomous vehicles lack effective methods to determine risks associated with road segments before navigation, leading to delayed awareness of potential hazards and compromised safety.
Innovation Solution
The system determines similarities between road segments using sensor data, map data, and metadata to infer scenario information and risk profiles, allowing for the classification of road segments and the association of risk profiles with unclassified segments, enabling informed navigation and fleet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system collects and processes sensor data from multiple vehicles to determine road segment features and scenarios, then the reliability and accuracy of risk assessment is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of risk assessment into distinct components: feature determination (extracting road segment characteristics from sensor data), scenario determination (identifying potential hazard situations), and similarity determination (comparing current segments with historical data). This segmentation allows each component to be processed independently, improving reliability while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data to determine road segment features and scenarios before navigation decisions are made. Historical sensor data from multiple vehicles is collected and analyzed in advance to build a database of road segment characteristics, enabling faster and more reliable risk assessment when vehicles encounter similar segments.
2Measurement precision
If the system determines similarities between road segments using multiple data sources, then the measurement precision of risk identification is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary determination of road segment features, scenarios, and similarities using historical sensor data from multiple vehicles before real-time navigation decisions are required. By pre-processing and storing this information in a database, the system achieves high measurement precision for risk identification while minimizing time loss during actual navigation, as the comparative analysis data is already prepared.
3Adaptability or versatility
If the system maintains a comprehensive scenario information database with associations between sensor data, road segments, and risk profiles, then the adaptability to different road conditions is improved, but the quantity of data storage requirements increase
Solution Approach 1:
The scenario information database is designed with universal structure that can accommodate multiple types of sensor data, road segment characteristics, and risk profiles in a unified framework. The database stores associations between diverse data sources (sensor readings, map data, metadata) and various road conditions, enabling the system to adapt to different scenarios while using a single multi-functional storage system rather than separate databases for each data type.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine a set of features associated with a road segment based at least in part on data captured by one or more sensors of a vehicle. At least one scenario that is associated with the set of features can be determined. The at least one scenario can be associated with the road segment. The associated at least one scenario and the road segment can be maintained in a scenario information database.