LiDAR Parameter Adjustment for Dynamic Scenario Adaptation
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Solution Overview
Problem
Current LiDAR systems operate with fixed parameters, such as working frequency, detection angle, and range, which are not dynamically adjustable, limiting their adaptability to varying scenarios and environments.
Innovation Solution
A method and device that acquire 3D environment information to identify scenario types and drivable areas, using neural networks to process point cloud data and adjust LiDAR parameters like horizontal and vertical angles of view, scanning frequency, and pulse emitting power based on the identified scenario and area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LiDAR maintains fixed working parameters (frequency, detection angle, range), then device complexity is reduced and ease of operation is improved, but adaptability to different scenarios deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment by enabling the LiDAR to automatically modify its working parameters (detection angle, range, frequency) based on real-time scenario recognition. The system transitions from static fixed parameters to dynamic adaptive parameters through scenario-type identification and corresponding parameter adjustment strategies.
Solution Approach 2:
The patent applies parameter changes by systematically adjusting multiple LiDAR operating parameters according to different scenario types. The system identifies scenarios (e.g., highway, urban, parking) and automatically modifies parameters such as detection angle, range, and frequency to optimize performance for each specific scenario.
2Productivity
If LiDAR uses fixed detection parameters, then energy consumption is reduced and device simplicity is maintained, but detection efficiency and obstacle detection rate deteriorate in varying environments
Solution Approach 1:
The patent implements partial action by adjusting LiDAR parameters dynamically based on scenario requirements. Instead of maintaining maximum detection capabilities in all scenarios, the system applies appropriate detection parameters only when needed for specific scenario types, avoiding excessive energy consumption in scenarios where full detection capability is not required.
Solution Approach 2:
The system dynamically adjusts detection parameters to match scenario demands, transitioning from static full-capability operation to dynamic demand-based operation. This enables the LiDAR to optimize the balance between detection efficiency and energy consumption by adapting parameters to actual environmental requirements.
3Adaptability or versatility
If LiDAR parameters are dynamically adjusted based on scenario type, then adaptability and detection efficiency are improved, but device complexity and control difficulty increase
Solution Approach 1:
The patent implements self-service through automatic scenario recognition and autonomous parameter adjustment. The LiDAR system independently identifies the current scenario type and automatically selects and applies the appropriate parameter adjustment strategy without requiring manual intervention, thereby maintaining ease of operation while achieving high adaptability.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring the environment, identifying scenario types, and adjusting parameters based on this information. The closed-loop control process involves scenario recognition feedback triggering automatic parameter adjustments, which then optimize detection performance for the identified scenario.
Data Source
AI summary
A method and a device for adjusting parameters of LiDAR and a LiDAR are provided. The method includes: acquiring 3D environment information around the LiDAR; identifying a scenario type where the LiDAR is positioned and a drivable area based on the 3D environment information; determining a parameter adjusting strategy of the LiDAR based on the scenario type and the drivable area; and adjusting current operating parameters of the LiDAR based on the parameter adjusting strategy.