LiDAR Drop Strategy for Congested Point Cloud Channels
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Solution Overview
Problem
Autonomous vehicles equipped with LIDAR devices face communication channel congestion due to the generation of more data points than can be transmitted, leading to timely and unsuccessful transmission of critical environmental data, which can hinder smooth operation.
Innovation Solution
A computing system associated with the LIDAR device engages in a drop process by discarding or preventing the emission of certain data points or light pulses based on an intelligent strategy determined by analyzing the vehicle's environment, operation, and LIDAR device operation, to alleviate channel congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the LIDAR device generates and transmits all data points representing detected return light pulses, then the completeness of environmental data is improved, but the communication channel becomes congested and transmission reliability deteriorates
Solution Approach 1:
The patent extracts and transmits only the most critical data points representing essential environmental features, while discarding redundant or less important data points. This selective extraction approach ensures that the limited communication bandwidth is used efficiently to transmit only the most valuable information, maintaining transmission reliability while preserving essential environmental data completeness
Solution Approach 2:
The patent applies different transmission priorities to different data points based on their importance. Critical data points representing obstacles, pedestrians, or other safety-relevant features are transmitted with higher priority, while less critical data points are discarded or transmitted with lower priority. This local quality differentiation resolves the contradiction by ensuring reliable transmission of essential data without being bottlenecked by non-essential data
2Loss of information
If the LIDAR device transmits all generated data points, then data completeness is improved, but bandwidth consumption increases and transmission timeliness deteriorates
Solution Approach 1:
The patent extracts only the most time-critical data points for immediate transmission, discarding less urgent data points. By identifying and transmitting only essential data points that require immediate processing, the system maintains transmission timeliness while preserving sufficient data completeness for safe autonomous operation
Solution Approach 2:
The patent implements periodic assessment of data point importance and selective transmission based on current operational context. The system continuously evaluates which data points require immediate transmission versus which can be discarded or transmitted later, creating a dynamic, periodic filtering process that balances timeliness and completeness
3Loss of energy
If the LIDAR device prevents emission of certain light pulses to reduce data points, then bandwidth consumption is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies selective light pulse emission based on local environmental requirements. The system identifies regions of interest where precise measurement is critical (such as areas with detected obstacles or moving objects) and maintains full LIDAR scanning in those regions, while reducing or preventing light pulse emission in less critical regions. This local quality approach preserves measurement precision where needed while reducing overall bandwidth consumption
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 ensures that only useful data points are transmitted, reducing bandwidth consumption and maintaining the integrity of environmental data transmission, thereby enhancing the vehicle's autonomous operation.
Implementation Method 1
Individual points in the point cloud can be determined by emitting a light pulse and detecting a returning light pulse, if any, reflected from an object in the environment
Implementation Method 2
determining the distance to the object according to the time delay between the emitted light pulse and the detection of the reflected returning light pulse
Data Source
AI summary
Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.