Cognitive Lidar Scan Pattern Optimization via Reinforcement Learning
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Solution Overview
Problem
Current lidar systems in autonomous vehicles struggle to effectively utilize model-based optimization techniques to pursue higher-level goals such as autonomous path planning and collision avoidance, as these systems primarily provide sensor data for object detection and road boundary interpretation, lacking explicit models to integrate higher-level objectives.
Innovation Solution
Implementing a hierarchical architecture based on model-free reinforcement learning (RL) to adjust optimization parameters in real-time, using cognitive circuits that generate reward signals for lower-level tasks, allowing the lidar system to optimize scan patterns according to objectives like velocity, acceleration, coverage, and region-of-interest, while adhering to constraints like maximum velocity and acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based optimization techniques are used to optimize scan patterns, then manufacturing precision and measurement precision improve, but device complexity increases due to the need for explicit models integrating higher-level objectives
Solution Approach 1:
The patent replaces model-based optimization with a model-free reinforcement learning approach. Instead of using explicit mathematical models to optimize scan patterns, the system uses neural networks trained through reinforcement learning to directly generate scan patterns that achieve higher-level goals such as collision avoidance and path planning, thereby reducing system complexity while maintaining detection precision
Solution Approach 2:
The patent introduces cognitive circuits as an intermediary layer between the sensor system and the control architecture. These cognitive circuits process sensor data and generate cognitive outputs that inform scan pattern generation, serving as a mediator that enables the integration of higher-level objectives without requiring complex explicit models throughout the entire system
2Area of stationary object
If the scanner covers the maximum available field of regard, then area of coverage increases, but productivity decreases due to scanning regions that do not contain useful information
Solution Approach 1:
The patent applies local quality by varying the scanning density across different regions of the field of regard. The reinforcement learning-optimized scan patterns allocate more scanning resources to regions containing useful information (such as regions with detected objects or potential hazards) and reduce scanning in regions that do not contain useful information (such as clear sky or empty road surfaces), thereby improving scanning efficiency while maintaining necessary coverage
Solution Approach 2:
The patent makes the scan pattern dynamic by using reinforcement learning to adaptively adjust scanning parameters in real-time based on the current environment. The scan patterns change dynamically to respond to moving objects, changing road conditions, and evolving traffic situations, allowing the system to optimize both coverage and efficiency as conditions change
3Measurement precision
If the system rescans regions of interest multiple times, then measurement precision improves, but loss of time increases due to repeated scanning of the same areas
Solution Approach 1:
The patent applies partial action by selectively rescanning only specific regions of interest rather than performing exhaustive rescanning of the entire field of regard. The reinforcement learning system identifies which regions require additional scanning based on detection confidence, object importance, and safety considerations, performing partial rescanning that improves detection precision for critical regions while minimizing time loss
4Productivity
If the scanner operates at maximum velocity to improve productivity, then scanning speed increases, but measurement precision deteriorates due to reduced data density
Solution Approach 1:
The patent makes the scanning velocity dynamic by using reinforcement learning to adaptively adjust the scanner speed based on real-time conditions. The system operates at maximum velocity when scanning clear regions to maintain high productivity, but automatically reduces velocity when scanning regions containing objects or potential hazards to ensure sufficient data density for accurate detection, thereby dynamically optimizing the trade-off between scanning speed and measurement precision
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 enables the lidar system to generate scan patterns that optimize multiple objectives, including collision avoidance and path planning, by leveraging reinforcement learning to adjust parameters based on feedback from the control architecture, enhancing the vehicle's ability to navigate and detect lane boundaries effectively.
Implementation Method 1
a light source configured to emit light pulses; a scanner configured to scan a field of regard (FOR) of the lidar system including direct the light pulses at different angles toward different points within the FOR
Data Source
AI summary
A method for determining a scan pattern according to which a sensor equipped with a scanner scans a field of regard (FOR) is presented. The method comprises obtaining, by processing hardware, a plurality of objective functions, each of the objective functions specifying a cost for a respective property of the scan pattern, expressed in terms of one or more operational parameters of the scanner. The method further includes applying, by the processing hardware, an optimization scheme to the plurality of objective functions to generate the scan pattern. The method further includes scanning the FOR according to the generated scan pattern.