Vehicle Sensor Calibration via Induced Motion
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Solution Overview
Problem
Current sensor calibration techniques for autonomous vehicles require infrastructure and are computationally expensive, leading to undesirable downtime and potential safety risks due to the need for external calibration methods.
Innovation Solution
The method involves inducing motion in the vehicle using an active suspension system or external forces to capture sensor data at different positions, allowing for the determination of calibration errors without external infrastructure, enabling independent calibration of multiple sensors and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrastructure-based calibration methods are used, then calibration accuracy can be achieved, but system downtime increases and safety risks arise due to the need to travel to calibration locations
Solution Approach 1:
The system performs calibration autonomously using its own sensors and processing capabilities. The sensor calibration system captures sensor data, determines calibration errors, and adjusts calibration parameters without requiring external infrastructure or human intervention, enabling the system to calibrate itself while in operation or during normal operation pauses.
Solution Approach 2:
The calibration process extracts the essential calibration information directly from sensor data captured in the operational environment, removing the dependency on external calibration infrastructure. The system extracts calibration parameters from natural scene data or test patterns captured by the sensors themselves, eliminating the need to travel to specialized calibration locations.
2Measurement precision
If infrastructure-based calibration methods are used, then calibration can be performed, but device complexity and computational cost increase
Solution Approach 1:
The system uses its existing sensors and processing units to perform calibration, leveraging resources already present in the autonomous vehicle rather than requiring separate calibration equipment. The sensor calibration system processes sensor data through algorithms that determine calibration errors and adjust parameters using the vehicle's own computational infrastructure.
Solution Approach 2:
The sensor calibration system serves multiple functions: it calibrates sensors, validates calibration accuracy, and can operate in different modes (e.g., using natural scenes or test patterns). The same system components used for normal operation are utilized for calibration, eliminating the need for specialized calibration hardware and reducing overall system complexity.
3Measurement precision
If the system travels to calibration locations with infrastructure, then calibration can be performed, but safety risks increase due to potentially unsafe travel conditions
Solution Approach 1:
The system performs calibration autonomously using its own sensors and processing capabilities. The sensor calibration system captures sensor data, determines calibration errors, and adjusts calibration parameters without requiring external infrastructure or human intervention, enabling the system to calibrate itself while in operation or during normal operation pauses.
Solution Approach 2:
The calibration process extracts the essential calibration information directly from sensor data captured in the operational environment, removing the dependency on external calibration infrastructure. The system extracts calibration parameters from natural scene data or test patterns captured by the sensors themselves, eliminating the need to travel to specialized calibration locations.
4Adaptability or versatility
If existing calibration techniques are used to mitigate infrastructure requirements, then some calibration can be performed, but computational expense increases significantly
Solution Approach 1:
The system performs calibration autonomously using its own sensors and processing capabilities. The sensor calibration system captures sensor data, determines calibration errors, and adjusts calibration parameters without requiring external infrastructure or human intervention, enabling the system to calibrate itself while in operation or during normal operation pauses.
Solution Approach 2:
The system uses partial calibration approaches that focus on determining calibration errors rather than performing complete recalibration. By using test patterns or natural scenes with known features, the system performs sufficient calibration to maintain accuracy without the full computational burden of complete calibration procedures.
Data Source
AI summary
Motion can be induced at a vehicle, e.g., by actuating components of an active suspension system, and first sensor data and second sensor data representing an environment of the vehicle can be captured at a first position and a second position, respectively, resulting from the induced motion. A second sensor can determine motion information associated with the first position and the second position. Calibration information about the sensor, the first sensor data, and the motion information can be used to determine an expectation of sensor data at the second position. A calibration error can be the difference between the second sensor data and the expected sensor data.