Gaussian Calibration for Autonomous Vehicle Lateral Deviation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately and efficiently correcting lateral deviations from a reference line, particularly due to noisy camera-based detection methods, which require effective filtering and real-time calibration without relying on additional sensors like maps or GPS.
Innovation Solution
A computer-implemented method using a Gaussian distribution model to update calibrated deviation values based on camera-captured data, generating control signals to correct lateral deviations, allowing the vehicle to adjust its position relative to the lane center, thereby stabilizing the control system and improving navigation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If camera-based detection is used to determine lateral deviation, then the system cost is reduced and sensor complexity is minimized, but the measurement precision deteriorates due to noisy sensor data
Solution Approach 1:
A Gaussian distribution model is introduced as an intermediary between the noisy camera-based lateral deviation detection and the control system. The model filters and calibrates the raw sensor data by fitting a Gaussian curve to multiple detection points, extracting a more precise mean value that represents the true lateral deviation while eliminating noise and outliers.
Solution Approach 2:
The system transforms the raw lateral deviation measurements into calibrated deviation values by changing the parameter representation. Instead of using individual noisy measurement points, the system uses statistical parameters (mean and standard deviation) of the Gaussian distribution fitted to multiple measurements, thereby improving measurement precision through parameter transformation.
2Measurement precision
If real-time calibration is performed using Gaussian distribution model, then the navigation accuracy is improved, but the computational time and processing complexity increase
Solution Approach 1:
The system performs calibration on a selective basis rather than continuously. By using a sliding window approach and only recalibrating when necessary (when the distribution characteristics change significantly), the system achieves real-time accuracy improvement while minimizing unnecessary computational overhead and processing time.
Solution Approach 2:
The system collects multiple lateral deviation measurement points in advance before performing the Gaussian distribution fitting and calibration. This preliminary data collection allows the calibration process to work with a sufficient dataset, improving accuracy while enabling batch processing that reduces overall computational time compared to continuous single-point calibration.
3Reliability
If multiple deviation values are collected and calibrated using Gaussian distribution, then the reliability of lateral position determination is improved, but the quantity of data processing increases
Solution Approach 1:
The system extracts only the essential characteristics from the multiple deviation values by fitting a Gaussian distribution and extracting the mean and standard deviation parameters. This extraction process transforms a large quantity of raw data into a small set of meaningful parameters that capture the essential information about lateral position while discarding redundant and noisy data.
Solution Approach 2:
The system changes the data representation from multiple individual deviation measurements to a condensed parametric form (Gaussian distribution parameters). This parameter change reduces the data quantity from N individual measurements to just 2-3 key parameters (mean, standard deviation, and possibly skewness), making data processing more efficient while maintaining or improving reliability.
Data Source
AI summary
A computer-implemented method, apparatus, and system for receiving and calibrating lateral deviation values and for controlling an autonomous vehicle to correct the lateral deviation is described. In every perception and planning cycle, a single lateral deviation value representative of an estimated autonomous vehicle lateral deviation from a reference line (e.g., corresponding to a center of the lane) is generated based on camera detection. The deviation value for a present cycle is received. A calibrated deviation value can be updated for the present cycle based on the received deviation value and a Gaussian distribution model. Control signals for the present cycle are generated to drive the autonomous vehicle to at least partially correct the autonomous vehicle lateral deviation based on the updated calibrated deviation value.