Time-of-Flight Sensor Power Control via Multi-Exposure Saturation Metrics
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Solution Overview
Problem
Time-of-flight sensors face unreliability in environments with varied lighting and high reflectivity, leading to saturation and inaccurate data, which can hinder obstacle detection in autonomous vehicles, potentially reducing safety.
Innovation Solution
Implementing a method that uses multiple exposures with varying illumination and integration times to determine saturation levels, allowing for dynamic power control and pixel filtering to improve data quality, specifically by capturing a primary exposure with potential saturation and a secondary exposure with reduced saturation, and adjusting sensor parameters based on saturation metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sensor uses a single exposure with high illumination intensity to capture detailed data, then measurement precision is improved, but saturation occurs in high-reflectivity environments causing data loss
Solution Approach 1:
The patent divides the sensor's exposure operation into multiple segments with different exposure parameters. Instead of using a single exposure setting, the sensor captures multiple frames with varying illumination intensities and integration times, then selects or combines the most reliable data from these segments to avoid saturation while maintaining measurement precision.
Solution Approach 2:
The patent implements dynamic adjustment of exposure parameters based on real-time scene conditions. The sensor dynamically changes illumination intensity and integration time between frames, adapting to varying lighting conditions and object reflectivity properties to prevent saturation while ensuring accurate data capture.
2Measurement precision
If the sensor increases processing time to handle unreliable pixel data, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary filtering of pixel data during the capture phase by using multiple exposures with different parameters. Unreliable pixels are identified and filtered out before main processing occurs, so that subsequent processing only needs to handle reliable data, maintaining both precision and productivity.
Solution Approach 2:
The sensor system performs self-diagnosis and self-correction by analyzing its own captured data quality. It automatically identifies saturated or unreliable pixels and adjusts subsequent capture parameters or filters data without requiring external intervention or complex post-processing, thus maintaining processing efficiency while ensuring data reliability.
3Reliability
If the sensor uses multiple exposures with varying parameters, then reliability in varied lighting conditions is improved, but device complexity increases
Solution Approach 1:
The patent changes operational parameters (illumination intensity, integration time) of the existing sensor rather than introducing new hardware components. By systematically varying these parameters across multiple exposures, the system achieves reliable performance in varied lighting conditions while avoiding the complexity of additional sensors or complex optical systems.
Solution Approach 2:
The system uses feedback from the captured data quality to automatically adjust exposure parameters for subsequent frames. By monitoring indicators such as pixel saturation levels and signal-to-noise ratio, the sensor dynamically optimizes its operation, reducing the need for complex manual control systems while improving reliability across different lighting conditions.
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 enhances the reliability and accuracy of sensor data, reducing processing time and improving obstacle detection, thereby enhancing safety and navigation in autonomous vehicles.
Implementation Method 1
Time-of-flight sensors may be unreliable in certain environments
Implementation Method 2
reflections off objects that are extremely close to the sensor, reflections off objects that have high reflectivity
Data Source
AI summary
Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over-exposure or saturation. In some examples, multiple exposures captured at different exposure times can be used to determine an overall saturation value or metric representative of the sensor data. The saturation value may be used to control parameters of the sensor. For instance, the saturation value may be used to determine power control parameters for the sensor, e.g., to reduce over- and/or under-exposure.