Autonomous Vehicle Sensor Stream Timing Calibration for LIDAR Alignment
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately calibrating sensor streams due to varying internal latencies and communication protocols among different sensors, leading to misaligned data that can result in significant errors in interpreting the vehicle's motion and environment, especially at high speeds.
Innovation Solution
A method for temporally calibrating sensor streams by deriving and applying offset times to align data from inertial measurement units (IMU), wheel sensors, and LIDAR sensors, using techniques such as dead reckoning and computer vision to minimize differences in longitudinal accelerations and velocities, ensuring that all sensors represent the same instant in time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected without temporal calibration, then data collection is simple and fast, but measurement precision deteriorates due to misaligned sensor readings
Solution Approach 1:
The patent applies parameter changes by adjusting temporal parameters (time offsets) of sensor data to achieve alignment. The system derives longitudinal accelerations and velocities from multiple sensors at different time points and calculates optimal time offsets that minimize differences between sensor readings, thereby improving measurement precision through temporal parameter adjustment.
Solution Approach 2:
The patent implements feedback by using derived sensor measurements (accelerations and velocities) to calculate and adjust time offsets. The system continuously compares sensor data, determines temporal misalignment, and applies corrective time offset adjustments to minimize differences between synchronized sensor readings, creating a closed-loop calibration process.
2Productivity
If high-speed vehicle operation is maintained, then productivity is high, but measurement precision deteriorates due to amplified sensor timing errors
Solution Approach 1:
The patent addresses high-speed operation challenges by dynamically adjusting temporal parameters based on vehicle speed and acceleration. The system derives accelerations and velocities from multiple sensors and calculates time offsets that compensate for speed-related timing errors, maintaining measurement precision even during high-productivity fast operation.
3Adaptability or versatility
If multiple sensor types are integrated, then adaptability improves, but device complexity increases due to varying internal latencies and communication protocols
Solution Approach 1:
The patent uses an intermediary approach by introducing a central processing system that acts as a mediator between multiple sensors with different latencies and protocols. This intermediary derives measurements from each sensor, calculates appropriate time offsets, and synchronizes the data streams, thereby managing complexity while maintaining multi-sensor adaptability.
Solution Approach 2:
The patent applies parameter changes by adjusting temporal parameters (time offsets) for each sensor type based on their specific characteristics. The system derives accelerations and velocities from different sensor streams and calculates individual time offsets that minimize differences between synchronized readings, enabling adaptable multi-sensor integration without proportionally increasing complexity.
4Measurement precision
If temporal calibration is performed, then measurement precision improves, but loss of time increases due to calibration processing
Solution Approach 1:
The patent applies preliminary action by performing temporal calibration continuously in the background during normal operation. The system derives accelerations and velocities from sensor data and calculates time offsets proactively, so that calibration is already complete before critical decision-making requires synchronized data, minimizing the impact on operational timing.
Data Source
AI summary
One variation of a method for temporally calibrating sensor streams in an autonomous vehicle includes: deriving a first set of longitudinal velocities of a reference point on the autonomous vehicle from a sequence of inertial data recorded over a period of time; deriving a second set of longitudinal velocities of the reference point based on features detected in a set of LIDAR frames recorded during the period of time; calculating a LIDAR sensor offset time that approximately minimizes a difference between the first set of longitudinal velocities and the second set of longitudinal velocities; and, in response to the LIDAR sensor offset time approximating an previous LIDAR sensor offset time, verifying operation of the LIDAR sensor during the first period of time.