Road Object Visibility Prediction from Light Source Orientation
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Solution Overview
Problem
Modern vehicles equipped with sensors often fail to detect road objects like road markings due to visibility issues caused by light sources, leading to incomplete map data and potential safety hazards.
Innovation Solution
A system that predicts the state of visibility for road objects by calculating the orientation of light sources and determining the state of artificial light sources, using temporal data and attribute data to generate data points for updating map layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to detect road objects, then map data can be updated and contextual information can be provided, but sensors may fail to detect road objects due to visibility issues caused by light sources
Solution Approach 1:
The system performs preliminary analysis of light source characteristics and temporal patterns before detection failures occur. By pre-processing temporal data from multiple sensors and calculating expected visibility conditions, the system prepares prediction models in advance that can compensate for upcoming detection failures caused by glare, darkness, or other light-related obscuration events.
Solution Approach 2:
The system introduces an intermediary prediction layer between the raw sensor data and the final detection output. This intermediary component analyzes temporal patterns and light source attributes to generate visibility predictions that mediate between conflicting sensor readings, resolving detection uncertainties caused by light source interference without requiring direct sensor observation of obscured road objects.
2Loss of information
If sensors fail to detect road objects, then visibility prediction can be provided to explain the failure, but incomplete map data and safety hazards remain
Solution Approach 1:
The system implements feedback loops where detection failures are fed back into the prediction model for continuous improvement. When sensors fail to detect road objects, the system uses the predicted visibility state as feedback to refine its models, and this learned information is used to update map data with confidence indicators that reflect detection reliability, thereby maintaining map completeness even when direct observation is unavailable.
Solution Approach 2:
The system creates virtual copies of road object data through prediction models when direct sensor detection fails. By generating predicted visibility states and confidence levels that mirror what would be observed under ideal conditions, the system maintains complete map data structures with annotated reliability information, allowing navigation systems to plan routes that account for detected visibility hazards.
3Measurement precision
If temporal data and attribute data are processed to predict visibility, then accurate visibility predictions can be provided, but computational complexity increases
Solution Approach 1:
The system segments the visibility prediction process into distinct modular components: temporal data processing, attribute data analysis, light source characterization, and prediction synthesis. Each module handles specific aspects of the computation independently, allowing for optimized processing of each data type and enabling parallel computation that reduces overall complexity while maintaining high prediction accuracy through systematic breakdown of the analytical task.
Data Source
AI summary
An apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.