LiDAR Point Cloud Weather Detection via KL Divergence
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Solution Overview
Problem
Existing methods for detecting adverse weather conditions in autonomous vehicles using LiDAR sensors are inadequate, as they often require dedicated sensors, are sensitive to scene variations, and struggle with dynamic operational environments, leading to reduced performance and increased complexity.
Innovation Solution
Implementing a LiDAR point cloud assessment algorithm that uses a statistical approach, specifically calculating the Kullbeck-Leibler (KL) divergence to determine statistical differences in point cloud data, allowing for adverse weather condition detection without the need for training sets or complex models, and adapting to dynamic scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated sensors are used for adverse weather detection, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The LiDAR sensor performs dual functions: primary object detection for autonomous navigation and secondary adverse weather condition detection. By analyzing statistical properties of returned light signals, the same sensor infrastructure detects rain, fog, snow, and other weather conditions without requiring separate dedicated sensors, thereby resolving the contradiction between detection reliability and device complexity
Solution Approach 2:
The LiDAR sensor monitors its own performance characteristics and environmental interactions to self-diagnose adverse weather conditions. The system analyzes variations in signal return patterns, intensity distributions, and temporal characteristics to autonomously detect weather conditions, eliminating the need for external dedicated detection systems while maintaining reliable detection capability
2Measurement precision
If complex models and training sets are used for weather detection, then detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system detects weather conditions by monitoring changes in statistical parameters of LiDAR point cloud data, such as intensity distribution, spatial density, and temporal variability. By tracking parameter deviations from baseline conditions using simple statistical thresholds, the system achieves accurate weather detection without requiring computationally intensive complex models or training sets, thus resolving the contradiction between detection accuracy and energy consumption
3Adaptability or versatility
If the system adapts to dynamic operational environments, then versatility is improved, but system complexity increases
Solution Approach 1:
The system dynamically adapts to changing operational environments by continuously monitoring LiDAR signal characteristics and adjusting detection thresholds based on current conditions. The statistical analysis framework automatically accommodates variations in scene composition, lighting, and environmental factors without requiring manual reconfiguration or complex adaptive algorithms, thereby achieving versatility while maintaining system simplicity
Data Source
AI summary
Disclosed are systems and methods for detecting weather conditions at a LiDAR sensor level. In some aspects, a method includes calculating a reference probability mass function (PMF) of at least one field of a point cloud generated from reference scene responses received from a light detection and ranging (LiDAR) sensor; calculating a current PMF for the at least one field of the point cloud generated from a current scene response received from the LiDAR sensor; determining a statistical difference between the reference PMF and the current PMF using a Kullbeck-Leibler (KL) divergence calculation; and responsive to the statistical difference satisfying a threshold for the at least one field, flagging an environmental change in the current scene response.