Vehicle Sensor Miscalibration Detection Using LIDAR Height Comparison
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Solution Overview
Problem
Autonomous vehicle sensors can become miscalibrated, leading to inaccurate data capture and prolonged diagnosis processes, consuming significant resources and potentially compromising safety.
Innovation Solution
A method using LIDAR sensors and a calibration grid to partition data, calculating average height values from multiple sensors, and comparing them to detect miscalibration, reducing computational resources and maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive diagnosis is performed to detect miscalibrated sensors, then detection accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the sensor data analysis by partitioning the field of view into multiple regions and selecting only specific regions for calibration checks. Instead of analyzing all sensor data comprehensively, the system divides the environment into zones and targets particular regions that are most indicative of calibration status, thereby reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The patent extracts only the necessary subset of sensor data required for calibration detection. By identifying and isolating specific regions or features that are most relevant to calibration status, the system eliminates unnecessary data processing steps while preserving the ability to accurately detect miscalibration.
2Measurement precision
If comprehensive sensor data is processed for calibration detection, then detection accuracy is improved, but computational resources and memory usage increase
Solution Approach 1:
The system segments the computational task by dividing sensor data into regional partitions and processing only selected regions for calibration detection. This segmentation reduces the overall computational load and memory requirements while maintaining sufficient data for accurate calibration assessment.
Solution Approach 2:
The patent applies partial action by processing only a subset of available sensor data that is sufficient for calibration detection. Rather than exhaustively analyzing all sensor inputs, the system identifies and processes the minimum necessary data portion, reducing computational resource consumption while achieving the detection objective.
3Reliability
If multiple sensors are analyzed for calibration, then detection reliability is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent manages the complexity of analyzing multiple sensors by segmenting the analysis into regional partitions. Each region can be evaluated independently, allowing the system to leverage data from multiple sensors without creating a combinatorial explosion of processing complexity. The segmentation structure organizes the multi-sensor data in a manageable way.
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 efficiently detects miscalibrated sensors, reducing processing time, memory usage, and network congestion while ensuring accurate detection, thereby enhancing safety outcomes and reducing the collection of inaccurate data.
Implementation Method 1
capturing sensor data of an environment, the sensor data comprising first light detecting and ranging (LIDAR) data
Implementation Method 2
light detecting and ranging (LIDAR) data associated with a first LIDAR sensor
Data Source
AI summary
A vehicle control system includes various sensors. The system can include, among others, LIDAR, RADAR, SONAR, cameras, microphones, GPS, and infrared systems for monitoring and detecting environmental conditions. In some implementations, one or more of these sensors may become miscalibrated. Using data collected by the sensors, the system can detect a miscalibrated sensor and generate an indication that one or more sensors have become miscalibrated. For example, data captured by a sensor can be processed to determine an average height represented by the sensor data and compared to an average height of data captured by other sensors. Based on a difference in heights, an indication can be generated identifying a miscalibrated sensor.