Parameterized Intersection Model for Automated Driving Sensor Coverage
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Solution Overview
Problem
Existing automated driving technologies lack effective tools and processes for determining sensing system coverage requirements, especially in complex intersection scenarios, with no robust analysis techniques or theoretical datasets for intersection detection requirements.
Innovation Solution
A computer-implemented method and system for sensor coverage analysis that builds a parameterized model of any orthogonal intersection, expanding it to account for complexities like road curvature and angles, using a low-fidelity analytical model to estimate time for vehicle maneuvers and sensor placement, and integrating vehicle sensor semantic detections and high-definition maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-fidelity simulation model is used to accurately represent intersection scenarios, then the precision of sensor coverage analysis is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the complex intersection scenario into discrete parameterized components (intersection geometry, vehicle states, sensor configurations, environmental conditions). Each component is modeled independently with specific parameters, allowing the overall system to be analyzed through combinations of these segmented elements without requiring a monolithic high-fidelity simulation of the entire scene.
Solution Approach 2:
The patent employs parameterized models where key variables (intersection angles, road curvatures, vehicle positions, sensor fields of view) are represented as adjustable parameters. This allows the system to efficiently evaluate different scenarios by changing parameter values rather than rebuilding complex geometric models, maintaining analysis precision while reducing computational burden.
2Adaptability or versatility
If comprehensive intersection complexities (curvatures, angles, multiple lanes) are included in the model, then the adaptability to real-world scenarios is improved, but the difficulty of detecting and measuring sensor coverage increases
Solution Approach 1:
The patent creates a universal parameterized framework that can represent diverse intersection types (four-way, T-intersections, curved roads, multi-lane configurations) through a common set of parameters. This single adaptable model structure handles multiple intersection complexities without requiring separate specialized models for each scenario type, making the system versatile while maintaining measurement tractability.
3Reliability
If real-time sensor placement optimization is performed for automated driving decisions, then the safety of intersection maneuvers is improved, but the processing time required for decision-making increases
Solution Approach 1:
The patent performs preliminary sensor placement optimization and intersection scenario analysis offline to pre-compute optimal sensor configurations and coverage characteristics for various intersection types. These pre-computed results are stored as lookup tables or preprocessed data structures, enabling the system to quickly retrieve and apply optimal sensor placements in real-time without performing computationally intensive optimization during actual decision-making moments.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes defining, by a processing device, a plurality of parameters so that any orthogonal intersection can be described. The method further includes building, by the processing device, an orthogonal parameterized model that can represent any orthogonal intersection based at least in part on the plurality of parameters that can describe any intersection of interest. The method further includes expanding, by the processing device, the orthogonal parameterized model to generate a fully parameterized intersection model that accounts for intersection complexities. The method further includes building, by the processing device, a low-fidelity analytical that computes various metrics based on the fully parameterized intersection model.