LIDAR Channel Switching for Multi-Resolution Point Clouds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LIDAR systems with a large number of channels, such as 32 or 64 channels, generate denser point clouds that current computer-implemented control systems in autonomous vehicles are not equipped to process efficiently, leading to latency and the need to deactivate channels to maintain processing capabilities, which limits the utilization of increased resolution.
Innovation Solution
A computing system dynamically activates and deactivates channels of a spinning LIDAR system based on the environment and objects detected, allowing for varying resolutions within a point cloud without requiring updates to the control system, thereby utilizing the full potential of the LIDAR system while managing processing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR systems with a large number of channels (32 or 64 channels) are used to generate denser point clouds, then measurement precision and resolution are improved, but device complexity and processing requirements increase beyond the capability of current control systems
Solution Approach 1:
The patent segments the LIDAR system into multiple independent channels that can be selectively activated or deactivated. Each channel operates independently, allowing the system to divide the full 64-channel capability into manageable subsets (e.g., 16 active channels) that match the processing capacity of existing control systems, thereby reducing effective device complexity while preserving high-resolution capability when needed.
Solution Approach 2:
The patent implements dynamic channel activation and deactivation based on real-time environmental conditions and processing demands. The computing system can adjust which channels are active during operation, transforming the static system configuration into a dynamic one that adapts to varying resolution requirements and processing capacities, allowing the same hardware to operate at different effective resolutions.
2Measurement precision
If all channels of a LIDAR system are activated to maximize point cloud density, then measurement precision is improved, but processing time increases causing latency in autonomous vehicle control systems
Solution Approach 1:
The patent applies partial action by activating only a subset of available channels (e.g., 16 out of 64) rather than all channels simultaneously. This partial activation reduces the total number of points in the point cloud from what would be generated by full 64-channel operation, thereby reducing processing time and latency while still providing sufficient resolution for autonomous vehicle operation in many scenarios.
Solution Approach 2:
The patent changes the operational parameter of channel activation from a fixed all-or-nothing state to a variable state where the number of active channels can be adjusted. By dynamically changing this parameter based on environmental complexity and processing capacity, the system optimizes the balance between point cloud density and processing speed, reducing latency when full density is not required.
3Productivity
If channels are permanently deactivated to reduce processing load, then processing speed is improved, but measurement precision and resolution are degraded
Solution Approach 1:
The patent transforms the static permanent deactivation of channels into a dynamic selective activation model. Instead of permanently disabling channels, the system can activate additional channels when high resolution is needed (such as when pedestrians or obstacles are detected) while maintaining fast processing speeds during normal operation. This dynamic approach allows the system to achieve both high processing speed and high measurement precision at different times as needed.
Solution Approach 2:
The patent implements feedback mechanisms where the computing system monitors environmental conditions and processing performance, then adjusts channel activation accordingly. When the system detects that higher resolution is needed for safe operation (such as detecting complex environments or potential hazards), it activates additional channels to improve measurement precision. This feedback-driven approach ensures that processing speed is optimized for normal conditions while measurement precision is enhanced when safety requires it.
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 point clouds with higher resolution where needed, like in regions with pedestrians, while maintaining lower resolution in periphery areas, without introducing latency or requiring redesign of the control system, thus enhancing the vehicle's ability to process data from a larger number of channels.
Implementation Method 1
Each light emitter/light detector pair is referred to as a 'channel' of the LIDAR systems
Implementation Method 2
An exemplary LIDAR system mounted on or incorporated in autonomous vehicles may be a spinning LIDAR system
Data Source
AI summary
An autonomous vehicle having a LIDAR system mounted thereon or incorporated therein is described. The LIDAR system has N channels, with each channel being a light emitter/light detector pair. A computing system identifies M channels that are to be active during a scan of the LIDAR system, wherein M is less than N. The computing system transmits a command signal to the LIDAR system, and the LIDAR system performs a scan with the M channels being active (and N-M channels being inactive). The LIDAR system constructs a point cloud based upon the scan, and the computing system controls the autonomous vehicle based upon the point cloud.