Vehicle Obstacle Detection Performance Validation via Relative Pose
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Solution Overview
Problem
Existing obstacle detection systems in vehicles lack a practical and cost-effective method for validating their performance, especially in autonomous vehicles, due to the lack of suitable ground truth sensors and the high cost of existing validation methods.
Innovation Solution
A central unit evaluates the performance of obstacle detection systems by comparing obstacle detection data with high-integrity pose data from multiple objects within a confined area, using relative pose calculations to generate ground truth data for performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground truth sensors are used to validate obstacle detection performance, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces pose estimation data from localization systems as an intermediary reference standard. Instead of using expensive and complex ground truth sensors directly, the system uses pose data from multiple objects (including the ego vehicle and surrounding vehicles) to create a relative pose reference frame that serves as the ground truth for validating obstacle detection performance.
Solution Approach 2:
The patent creates a virtual copy of ground truth data by computing relative poses from localization information. Rather than relying on physical ground truth sensors, the system generates synthetic ground truth labels by calculating the relative positions and orientations of objects based on their localization data, which can be obtained from standard sensors already present in autonomous vehicles.
2Reliability
If extensive testing is conducted to validate obstacle detection performance, then reliability is improved, but loss of time and resources increase
Solution Approach 1:
The system uses the obstacle detection system's own output data (detection results) combined with pose estimation data to perform self-validation. By comparing detected obstacles against the ground truth derived from pose information, the system can autonomously evaluate its own performance without requiring external testing facilities or manual verification processes.
Solution Approach 2:
The patent implements a feedback mechanism where performance validation results are continuously fed back into the system. The validation process compares obstacle detection results against ground truth pose data, and this feedback information can be used to adjust detection parameters, retrain models, or identify performance degradation patterns, enabling continuous improvement without requiring separate validation testing phases.
3Measurement precision
If multiple sensors are deployed for obstacle detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the validation function into the existing multi-sensor obstacle detection system rather than adding separate validation hardware. By integrating pose estimation data processing and performance comparison logic into the same computational platform that processes sensor data, the system achieves validation capabilities without proportionally increasing hardware complexity.
Data Source
AI summary
A method performed by a central unit for evaluating a performance of an obstacle detection system comprised in a first object within a set of two or more objects at a confined area. The first object is a vehicle. The central unit obtains pose data from each object in the set and obstacle detection data from at least the first object. The central unit determines a relative pose of the objects using the pose data from each object. The central unit the obstacle detection data from each object in the set to the determined relative pose of the objects. Based on the comparison, the central unit evaluates the performance of the obstacle detection system of the first object.