Continuous Radar Calibration Checks with LiDAR Position Comparison
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently managing and processing large volumes of data from various sensors, leading to increased computational overhead and potential calibration issues that can affect driving safety.
Innovation Solution
A system that compares position data from LiDAR and radar sensors to identify changes in relative locations of objects, initiating corrective actions when these changes exceed a threshold, thereby maintaining sensor calibration and ensuring safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous calibration checks are performed using multiple sensor types (LiDAR, radar, cameras), then measurement precision and reliability are improved, but computational overhead and processing time increase
Solution Approach 1:
The system performs preliminary calibration checks by comparing sensor data at predefined check locations before full processing is required. This preliminary action allows the system to quickly verify calibration status without immediately triggering computationally intensive recalibration processes, thus maintaining measurement precision while preserving computational efficiency.
Solution Approach 2:
The system implements partial calibration checks by only evaluating specific parameters and selected sensor data points rather than processing complete sensor datasets continuously. This partial action approach maintains adequate calibration monitoring while significantly reducing computational overhead compared to full continuous calibration processing.
2Reliability
If continuous calibration checks are performed using multiple sensor types (LiDAR, radar, cameras), then reliability of sensor data is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary calibration checks by comparing sensor data at predefined check locations before full processing is required. This preliminary action allows the system to quickly verify calibration status without immediately triggering computationally intensive recalibration processes, thus maintaining measurement precision while preserving computational efficiency.
Solution Approach 2:
The system performs calibration checks at predefined check locations periodically rather than continuously processing all sensor data. This periodic action approach maintains reliable calibration monitoring while significantly reducing processing time by evaluating sensors only at specific intervals and locations during autonomous vehicle operation.
3Measurement precision
If multiple sensor types (LiDAR, radar, cameras) are integrated for comprehensive environmental perception, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the calibration and processing tasks by sensor type and by processing stage (preliminary checks vs. full processing). This segmentation allows each sensor type to be evaluated independently at appropriate intervals, maintaining comprehensive multi-sensor measurement precision while reducing overall system complexity through modular, manageable processing stages.
Solution Approach 2:
The system implements a universal calibration check framework that can accommodate multiple sensor types (LiDAR, radar, cameras) using a common processing architecture. This multi-functional approach maintains measurement precision across diverse sensors while reducing device complexity by providing a unified calibration and evaluation system rather than separate specialized systems for each sensor type.
Data Source
AI summary
Methods and apparatus consistent with the present disclosure may compare position data associated with a Light Detection and Ranging (LiDAR) device and location data associated with a radar device. Changes in relative locations of objects detected along a roadway over time may be used to identify whether sensing capabilities of a sensing apparatus are acceptable. Changes in positions detected by operation of a LiDAR device and locations detected by operation of a radar device may be considered normal or acceptable when those changes are below a threshold value. In instances when changes in relative positions/locations detected by operation the LiDAR device and the radar device at an AV change beyond a threshold value, a sensing system may be considered as being out of calibration. Once a processor detects that a sensing system has changed close to or beyond a threshold level, that processor may initiate a corrective action.