Multi-Resolution LiDAR Channel Control for Low-Latency Point Clouds
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Solution Overview
Problem
Current LIDAR systems with a large number of channels, such as 32 or 64 channels, generate denser point clouds that existing computer-implemented control systems in autonomous vehicles cannot efficiently process, leading to latency and the need to deactivate channels, which limits the system's ability to utilize the increased resolution effectively.
Innovation Solution
A dynamic channel activation and deactivation system in autonomous vehicles, where a computing system identifies active and inactive channels based on environmental factors like terrain, objects, and angular ranges, allowing for varying resolutions in different horizontal bands of the point cloud without requiring system redesign or introducing latency.
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 detection capability are improved, but existing control systems cannot efficiently process the increased data volume, leading to latency and system complexity issues
Solution Approach 1:
The patent segments the LIDAR system into multiple independent channels that can be dynamically activated or deactivated. Instead of processing all channels simultaneously, the system divides the point cloud data into manageable segments corresponding to active channels only, reducing the processing burden on control systems while maintaining high resolution where needed
Solution Approach 2:
The patent implements dynamic channel activation and deactivation based on real-time environmental conditions. The control system adaptively adjusts which channels are active during different operational scenarios, allowing the system to optimize between measurement precision and processing complexity dynamically rather than being fixed
2Reliability
If all channels of a multi-channel LIDAR system are activated to maximize point cloud density, then detection capability is improved, but processing time increases and latency is introduced
Solution Approach 1:
The patent applies partial action by activating only the necessary subset of channels required for current operational needs rather than all channels. The system determines the minimum required channels based on environmental factors, vehicle state, and detection requirements, processing only that portion of data needed to maintain reliable object detection while minimizing processing time
Solution Approach 2:
The patent changes the operational parameters of the LIDAR system by dynamically adjusting the number of active channels based on real-time conditions. This parameter change allows the system to transition between high-detection modes (more channels active) and low-latency modes (fewer channels active), optimizing the balance between reliability and processing speed
3Productivity
If channels are permanently deactivated to reduce data volume for control system processing, then processing efficiency is improved, but the system fails to utilize the increased resolution capability of multi-channel LIDAR systems
Solution Approach 1:
The patent transforms the static channel configuration into a dynamic one where channels can be activated or deactivated based on real-time operational requirements. This allows the system to adapt its resolution capability to match the actual processing capacity and environmental needs, rather than being permanently limited
Solution Approach 2:
The patent makes the LIDAR channel system multi-functional by enabling the same physical channels to serve different purposes at different times. Channels can be activated for high-resolution detection when needed and deactivated when processing efficiency is prioritized, making the system universally adaptable to various operational scenarios without permanent limitations
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
Enables the autonomous vehicle to process point clouds with higher resolution in critical areas while maintaining efficient processing demands, enhancing the vehicle's ability to detect objects and navigate without the need for redesigning the control system.
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.