Lidar Module Non-linear Merit Function Alignment
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Solution Overview
Problem
Autonomous vehicles face challenges in achieving optimal alignment of optical components in lidar systems due to interdependent parameters and sensitivity to external noise, leading to sub-par performance in object detection and navigation.
Innovation Solution
The implementation of a non-linear merit function and area scan method for active optical alignment of laser modules, which includes a laser diode array, micro-optics module, and drive motor to achieve uniform space-filling imaging and improve production tolerances and manufacturing yields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear merit function is used for optimal position calculation, then the calculation process is simple, but the alignment precision deteriorates due to parameter interdependence and noise sensitivity
Solution Approach 1:
The patent transforms the alignment optimization problem from a linear merit function to a non-linear merit function. This parameter change enables the system to handle interdependent parameters more effectively and resist external noise, thereby improving alignment precision while maintaining computational feasibility through iterative optimization algorithms.
Solution Approach 2:
The patent implements an iterative feedback mechanism where the non-linear merit function continuously evaluates alignment quality and adjusts parameters accordingly. This feedback loop allows the system to converge to optimal alignment positions by repeatedly refining parameter estimates based on measured performance, overcoming the limitations of single-step linear methods.
2Ease of operation
If active alignment is performed for each dependent component separately, then the alignment process is straightforward, but the overall system performance deteriorates due to parameter interdependence
Solution Approach 1:
The patent merges the alignment processes of multiple dependent components into a unified non-linear optimization framework. Instead of separately aligning each component, the system simultaneously optimizes all component positions by evaluating their combined effect on the non-linear merit function, thereby accounting for parameter interdependence and improving overall system reliability.
Solution Approach 2:
The patent creates a universal alignment methodology that handles multiple dependent components through a single non-linear merit function evaluation process. This multi-functional approach can accommodate various component configurations and interdependence relationships, providing a robust solution that maintains high system performance across different operational scenarios.
3Speed
If gradient descent is used for position optimization, then the convergence is systematic, but the process is slow due to low resistance to external noise
Solution Approach 1:
The patent modifies the optimization parameters by switching from a linear merit function to a non-linear merit function. This parameter change fundamentally alters the optimization landscape, creating a more robust evaluation metric that is less sensitive to external noise while maintaining systematic convergence properties through iterative refinement.
Solution Approach 2:
The patent enhances the feedback mechanism in gradient descent by using a non-linear merit function that provides more reliable gradient information. This improved feedback allows the optimization process to better distinguish between actual alignment errors and noise-induced variations, enabling faster and more reliable convergence even in noisy environments.
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 resistance to external noise, reduces processing time, and ensures high-quality object detection and navigation by eliminating gaps in laser coverage, resulting in improved detection and classification capabilities for autonomous vehicles.
Implementation Method 1
a laser diode array configured to emit corresponding laser pulses
Implementation Method 2
a micro-optics module configured to focus the laser pulses into a scanning beam
Data Source
AI summary
An optical transmitter including a laser diode array configured to emit corresponding laser pulses; a micro-optics module configured to focus the laser pulses into a scanning beam; and a drive motor configured to rotate the optical transmitter so the scanning beam covers a horizontal field of view.