Distributed Sensor Calibration via CV2X Communication
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Solution Overview
Problem
Vehicular sensors face reliability issues due to radar interference, RF shadows, optical shadows, and inaccurate calibration, which can lead to distorted environmental representations and potentially catastrophic events, such as collisions, due to their inability to accurately convert location information from a source reference frame to a common reference frame.
Innovation Solution
The implementation of distributed sensor calibration and sharing using cellular vehicle-to-everything (CV2X) communication, where vehicles obtain calibration data from external sources like infrastructure sensors and neighboring vehicles to update their onboard sensor calibration tables, ensuring accurate conversion of sensor-derived location information to a common reference frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed sensor calibration using CV2X communication is implemented, then measurement precision and reliability of sensor data are improved, but device complexity and communication overhead increase
Solution Approach 1:
The patent uses CV2X communication as an intermediary mechanism to enable calibration data exchange between vehicles and infrastructure. The calibration server acts as a mediator that collects calibration data from multiple sources, processes it, and distributes updated calibration parameters to vehicles, thereby improving measurement precision without requiring direct complex interactions between all system components.
Solution Approach 2:
The system implements feedback loops where sensor calibration data is continuously collected from vehicles, processed to identify calibration drift or errors, and used to generate updated calibration parameters that are fed back to vehicles. This closed-loop feedback mechanism progressively improves measurement precision over time while automating the calibration process.
2Reliability
If real-time sensor calibration updates are performed, then reliability of sensor data is improved, but computing resource consumption increases
Solution Approach 1:
The calibration process is segmented into distinct phases: data collection at vehicles, data aggregation and processing at the calibration server, and parameter distribution back to vehicles. This segmentation allows computationally intensive operations to be performed at the server rather than in-vehicle systems, improving reliability while reducing onboard computing resource consumption.
Solution Approach 2:
The system performs preliminary processing of calibration data at the server before distribution to vehicles. Calibration parameters are pre-computed and validated in advance, so that vehicles receive ready-to-use calibration updates without needing to perform complex real-time computations, thereby reducing computing resource consumption while maintaining data reliability.
3Manufacturing precision
If comprehensive calibration data from multiple sources is collected, then manufacturing precision of sensor calibration is improved, but loss of time and communication overhead increase
Solution Approach 1:
The patent merges calibration data from multiple sources (vehicle sensors, infrastructure sensors, simulation data) into a unified calibration dataset processed by the calibration server. By combining these diverse data sources and processing them centrally, the system achieves higher calibration accuracy while managing communication overhead through efficient data aggregation and selective updates.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on the quality, quantity, and relevance of available data from different sources. By changing calibration parameters adaptively rather than using fixed update intervals, the system optimizes the balance between calibration accuracy and update frequency, reducing unnecessary communication overhead and time loss while maintaining high manufacturing precision.
Data Source
AI summary
Various aspects of the present disclosure generally relate to vehicle sensors. In some aspects, a device associated with a vehicle may obtain, from an external source, calibration data including a first set of measurements related to a position of one or more objects in a reference frame associated with the external source. The device may identify a second set of measurements related to the position of the one or more objects within a field of view of one or more onboard sensors, and update a calibration table based at least in part on a comparison of one or more of the first set of measurements and one or more of the second set of measurements that are most time-aligned. Accordingly, the device may convert sensor-derived location information from a source reference frame to a reference frame associated with the vehicle using the updated calibration table. Numerous other aspects are provided.