Optical Sensor Orientation Control for Vehicle Roll Correction
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Solution Overview
Problem
Existing computer vision techniques for vehicles are less effective when used with vehicles that experience significant rolling, such as motorcycles, due to the non-perpendicular view-up orientation of optical sensors, leading to lower detection accuracy.
Innovation Solution
A computer-implemented method that receives sensor data from vehicle sensors, filters noise, detects the amount of vehicle roll, generates a command to adjust the optical sensor's orientation, and controls the sensor's orientation to counteract the roll effect, thereby maintaining a perpendicular view-up orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-capture rotation and fitting of optical data is performed to correct roll effect, then view-up orientation is restored to perpendicular with road surface, but information loss occurs due to cropping of optical data
Solution Approach 1:
The patent applies preliminary action by correcting the roll effect on the optical sensor itself before data capture, rather than correcting the captured data afterward. The sensor is reoriented to maintain a perpendicular view-up orientation with the road surface during capture, preventing information loss that would occur with post-capture rotation and cropping.
Solution Approach 2:
The patent replaces the mechanical/post-processing approach of rotating and fitting captured images with a real-time sensor orientation control system. Instead of mechanically rotating images after capture (which causes cropping), the system uses feedback control to actively adjust the sensor's physical orientation during operation.
2Measurement precision
If optical sensor orientation is actively controlled to counteract vehicle roll, then detection accuracy is maintained on rolling vehicles, but system complexity increases due to additional control mechanisms
Solution Approach 1:
The patent implements feedback control by continuously monitoring the actual sensor orientation (via IMU data) and comparing it with the desired orientation, then adjusting the sensor orientation accordingly. This closed-loop feedback mechanism maintains detection accuracy on rolling vehicles while using standard control theory approaches to manage system complexity.
Solution Approach 2:
The patent introduces an intermediary control system that acts as a mediator between the vehicle's motion state and the optical sensor. This intermediary layer processes IMU data, calculates required orientation adjustments, and controls the sensor positioning, thereby managing the complexity of maintaining accuracy on rolling vehicles.
3Measurement precision
If computer vision techniques are trained using optical data from four-wheeled vehicles, then detection accuracy is very high on such vehicles, but effectiveness decreases significantly on motorcycles and other rolling vehicles
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the sensor's orientation parameters (roll, pitch, yaw) in real-time based on vehicle motion state. This allows the system to maintain the perpendicular view-up orientation assumption that computer vision algorithms expect, thereby maintaining high detection accuracy across different vehicle types including motorcycles.
Solution Approach 2:
The patent achieves universality by creating a sensor orientation control system that works across different vehicle types (four-wheeled vehicles, motorcycles, scooters). The same control methodology and algorithms can be applied to any vehicle, making the computer vision system universally adaptable while maintaining high detection accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach compensates for the roll effect in optical data without cropping information, providing more accurate orientation-appropriate data for image processing and computer vision systems, resulting in improved driver assistance and autonomous driving capabilities.
Implementation Method 1
acquiring optical data (e.g., capture images) of the environment around the vehicle using optical sensors
Implementation Method 2
performing exponential smoothing on the sensor data
Data Source
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AI summary
Embodiments of the present disclosure set forth techniques for compensating for a roll effect for an optical sensor. The techniques include receiving sensor data from at least one sensor associated with the vehicle, detecting an amount of a roll of the vehicle based on the sensor data, generating a command based on the detected amount of roll, and controlling an orientation of the optical sensor based on the command.