Vehicle Sensor Calibration via Probabilistic State Estimation
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Solution Overview
Problem
Current vehicle sensors, particularly low-cost sensors, suffer from time-varying offset and scale errors, noise, and drift, limiting their reliability for long-term prediction in advanced driver-assistance systems (ADAS) and autonomous vehicle applications, as existing calibration methods rely on simplistic averaging techniques that fail to account for dynamic driving conditions.
Innovation Solution
A system and method for real-time calibration of vehicle sensors, using a probabilistic approach to estimate sensor offsets and noise by modeling sensor measurements as stochastic distributions, incorporating a deterministic motion model and a probabilistic component to account for sensor uncertainties, allowing for iterative estimation of vehicle and sensor states without a pre-defined sensor calibration model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost sensors are used in vehicle, then cost is reduced, but measurement precision deteriorates due to offset errors, noise, and drift
Solution Approach 1:
The patent introduces a probabilistic state estimator as an intermediary computational system that processes sensor measurements to compensate for their inaccuracies. This estimator uses probabilistic models to separate true vehicle state from sensor errors, effectively mediating between low-cost sensors and reliable vehicle control decisions.
Solution Approach 2:
The patent transforms the sensor calibration problem from a static parameter adjustment to a dynamic parameter estimation process. By continuously estimating sensor offsets, noise characteristics, and scale factors as time-varying parameters during vehicle operation, the system adapts to changing sensor behavior without requiring expensive pre-calibration equipment.
2Measurement precision
If sensor calibration is performed beforehand, then initial accuracy is improved, but reliability deteriorates because sensor noise characteristics change with temperature, age, and placement
Solution Approach 1:
The patent transitions from static pre-determined sensor parameters to dynamic real-time estimation. The system continuously updates sensor noise characteristics, offsets, and calibration parameters during vehicle operation, allowing the calibration to adapt to changing environmental conditions, sensor aging, and temperature variations.
Solution Approach 2:
The patent implements a feedback mechanism where the probabilistic state estimator continuously monitors sensor measurements and uses the estimated vehicle state to infer and correct sensor calibration parameters. This closed-loop feedback ensures that sensor reliability is maintained over time by automatically compensating for drift and environmental effects.
3Device complexity
If simplistic averaging techniques are used for sensor calibration, then device complexity is reduced, but measurement precision deteriorates because they cannot estimate offsets during general driving conditions
Solution Approach 1:
The patent replaces complex mechanical calibration equipment and procedures with a computational probabilistic estimation system. Instead of using specialized calibration rigs and manual adjustment mechanisms, the system uses mathematical models and probability theory to achieve accurate sensor calibration during normal vehicle operation.
Data Source
AI summary
A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.