LiDAR Beam Configuration Optimization for Task Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Low-resolution LiDAR systems struggle to adapt their beam configuration for specific tasks, such as object detection and localization, due to their limited number of beams, which restricts their performance and requires more expensive, higher-resolution systems for adequate functionality.
Innovation Solution
A method is proposed to determine an optimal beam configuration for LiDAR systems by using an exploration step with a prediction function and exploration strategy, allowing for the selection of configurations that achieve better performance with fewer beams, thus enhancing the system's ability to perform specific tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of beams in a LiDAR system is increased to improve resolution and detection precision, then measurement precision and object detection capability are improved, but device cost and system complexity increase
Solution Approach 1:
The patent applies dynamics by making the LiDAR beam configuration adaptive and reconfigurable based on task requirements. Instead of using a fixed high-resolution configuration, the system dynamically selects optimal beam configurations from multiple pre-defined patterns, allowing low-resolution systems to achieve task-specific performance without permanently increasing system complexity
Solution Approach 2:
The patent changes the parameter of beam configuration from a fixed state to a variable state with multiple possible patterns. By exploring different beam configurations and selecting the optimal one for each task, the system achieves high measurement precision for specific tasks without requiring a permanently high-resolution (complex) system
2Device complexity
If a low-resolution LiDAR system uses a fixed beam configuration, then device complexity is reduced and cost is lowered, but adaptability to different tasks and measurement precision deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple beam configuration patterns before actual operation. The system prepares a set of candidate configurations in advance, allowing it to quickly adapt to different tasks without real-time complex optimization, thus maintaining low device complexity while improving task adaptability
Solution Approach 2:
The system transitions from a static fixed configuration to a dynamic reconfigurable system that can adapt its beam patterns based on task requirements. This dynamic capability enables low-resolution systems to perform multiple tasks effectively without increasing physical complexity
3Ease of manufacture
If the number of beams is reduced to lower system cost, then device cost and ease of manufacture are improved, but measurement precision and detection capability worsen
Solution Approach 1:
The patent changes the approach from increasing beam quantity to optimizing beam configuration parameters. By exploring and selecting optimal angular distributions and beam patterns, the system achieves task-specific measurement precision with fewer beams, making low-resolution systems manufacturable and cost-effective
4Reliability
If a LiDAR system uses more beams to cover a larger environmental portion, then detection coverage and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies segmentation by dividing the environmental detection task into multiple beam configuration patterns. Each pattern is optimized for specific detection scenarios, and the system segments the configuration space to cover different tasks reliably without requiring a single complex high-resolution system
Data Source
Figure 1~2
Figure 3A~4
Figure 5
AI summary
A method for determining an optimal configuration (CLB) of the beams (bi) of a LiDAR system (LiD) intended to perform a specific task, in which method: - an actual performance associated with a configuration corresponds to a quality of the result of an execution of said specific task by said LiDAR system (LiD) configured according to this configuration; - a predicted performance of a configuration is a prediction of an actual performance associated with this configuration; said method comprising: (i) an exploration step (E20); (ii) determining (E30) said optimal configuration (CLB) as being the configuration (CLn) having the best associated actual performance (VTn).