LiDAR Road Wetness Detection From Zero-Intensity Returns
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Solution Overview
Problem
Autonomous vehicles struggle to accurately detect the presence of water, snow, or other materials on the road surface, which affects traction and maneuverability, and do not adjust speed accordingly due to lacking human perception abilities.
Innovation Solution
A LiDAR system is used to generate point clouds, identify road surface points, and analyze zero intensity returns to determine the degree of wetness, with thresholds for no water, streaming water, and flooding, adjusting vehicle speed based on the detected wetness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If LiDAR is used to detect road surface conditions, then object detection capability is improved, but reliability deteriorates under atmospheric conditions like rain and fog due to light absorption and reflection
Solution Approach 1:
The patent combines multiple sensing technologies (LiDAR, RADAR, photographic imaging systems) into an integrated sensor system. This multi-sensor fusion approach compensates for the weaknesses of individual sensors under adverse atmospheric conditions, maintaining reliable detection capability when rain or fog is present by cross-validating data from different sensing modalities.
Solution Approach 2:
The sensor system is designed to perform multiple functions: detecting objects, measuring road surface wetness, and monitoring atmospheric conditions. By making the system universal and multi-functional, it can adapt to various detection needs and maintain reliability across different environmental conditions through flexible sensor selection and data fusion strategies.
2Extent of automation
If autonomous vehicles lack perception ability for road surface conditions, then automation is improved, but safety deteriorates due to inability to adjust speed for wet roads
Solution Approach 1:
The autonomous vehicle system performs self-diagnosis and self-adjustment by automatically detecting road surface wetness conditions and adjusting its driving parameters accordingly. The system serves itself by integrating sensors that monitor environmental conditions and autonomously modify speed and traction control without human intervention, maintaining safety while preserving automation.
Solution Approach 2:
The system implements continuous feedback loops where sensor data about road surface conditions is constantly monitored and fed back to the control system. This feedback enables the autonomous vehicle to dynamically adjust its driving behavior in real-time, ensuring safety is maintained despite high automation levels by constantly adapting to changing environmental conditions.
3Measurement precision
If LiDAR intensity data is analyzed for wetness detection, then measurement precision is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The LiDAR system is designed to serve multiple purposes: primary object detection and secondary road surface wetness measurement. By making the LiDAR system universal and multi-functional, the patent extracts additional useful information (intensity data for wetness detection) from the same sensor without requiring entirely separate detection systems, thereby improving measurement precision while limiting the increase in overall device complexity.
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
Enhances the ability of autonomous vehicles to safely navigate wet road conditions by accurately detecting water and adjusting speed, improving traction and maneuverability.
Implementation Method 1
A LiDAR sensor is configured to emit light, which strikes material (e.g., objects) within the vicinity of the LiDAR sensor. Once the light contacts the material, the light is deflected. Some of the deflected light bounces back to the LiDAR sensor.
Implementation Method 2
The LiDAR sensor is configured to measure data pertaining to the light bounced back (e.g., the distance traveled by the light, the length of time it took for the light to travel from and to the LiDAR sensors, the intensity of the light returning to the LiDAR sensor, etc.).
Data Source
AI summary
Systems and methods for determining a degree of wetness of a road surface from Light Detection and Ranging (LiDAR) point clouds are provided. The method comprises generating, using a LiDAR system, at least one point cloud, wherein the LiDAR system comprises a processor, and, using the processor, identifying and isolating one or more road surface points within a point cloud of the at least one point cloud, wherein the one or more road surface points indicate a road surface portion within an environment of the point cloud, analyzing the one or more road surface points to determine a number of the one or more road surface points that are zero intensity returns, and, based on the number of zero intensity returns, determining a degree of wetness of the road surface.