LIDAR Drop Strategy for Bandwidth-Limited Point Cloud Transmission
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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 the exclusion of critical environmental data from the computing device, which can impact smooth operation.
Innovation Solution
A dynamic drop process is implemented by the computing system associated with the LIDAR device, where data points or light pulses are selectively discarded or prevented based on environmental and operational analysis to alleviate channel congestion, ensuring only useful data is transmitted.
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 filtering out redundant or less important data points. This selective extraction approach maintains transmission reliability by reducing data volume while preserving the most valuable environmental information needed for autonomous vehicle operation.
Solution Approach 2:
The patent applies different quality standards to different data points based on their importance. Critical data points representing obstacles, pedestrians, or important environmental features are transmitted with high priority, while less critical data points are filtered or transmitted with lower priority. This local quality differentiation resolves the contradiction by ensuring reliable transmission of essential data without attempting to transmit all data equally.
2Loss of information
If the LIDAR device transmits all generated data points, then data completeness is improved, but bandwidth consumption increases and transmission efficiency decreases
Solution Approach 1:
The system extracts only the most essential data points from the complete set of LIDAR measurements, identifying and transmitting solely those data points that represent critical environmental information. This extraction process dramatically reduces bandwidth consumption while maintaining the completeness of functionally important data, thereby improving transmission efficiency.
Solution Approach 2:
The patent implements partial action by transmitting a subset of data points rather than the complete dataset. By sending only the necessary portion of data required for safe autonomous operation, the system achieves sufficient data completeness for its operational needs while significantly improving transmission efficiency and reducing bandwidth consumption.
3Measurement precision
If the computing device receives and processes all data points, then environmental analysis accuracy is improved, but processing time increases and system responsiveness decreases
Solution Approach 1:
The computing device extracts and processes only the most critical data points representing essential environmental features rather than processing the complete dataset. This selective processing approach maintains environmental analysis accuracy for important objects and scenarios while dramatically reducing processing time and improving system responsiveness to dynamic environmental changes.
Solution Approach 2:
The system applies different processing quality levels to different data points based on their importance. High-priority data points representing critical environmental features receive full processing attention to maintain accuracy, while lower-priority data points receive reduced or deferred processing. This local quality differentiation preserves analysis accuracy for essential elements while reducing overall processing time.
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 strategy effectively reduces bandwidth consumption, ensuring critical data points are retained and transmitted, thereby enhancing the vehicle's ability to navigate safely and efficiently by prioritizing relevant environmental information.
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
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.