Road Weather Detection via Laser Intensity Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting road weather conditions, such as wetness, using existing sensor data, which can impact safe operation and driving decisions.
Innovation Solution
A method and system that utilize laser data, camera images, precipitation sensors, and weather information to estimate road conditions by comparing intensity and distribution patterns to threshold values, and using Bayesian estimates to make informed driving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If laser data intensity is used to detect road weather conditions, then detection capability is improved, but measurement precision deteriorates due to intensity variations caused by factors other than wetness
Solution Approach 1:
The patent segments the laser detection task by separating road surface analysis from other objects. It identifies and excludes non-road objects (vegetation, buildings, sky) from the intensity calculation by comparing laser return intensities and spatial patterns, then focuses analysis only on road surface points to improve measurement precision.
Solution Approach 2:
The patent transforms the detection approach by changing from using absolute intensity values to using intensity distribution statistics (mean, standard deviation, percentiles). This parameter transformation makes the detection robust against variations in lighting conditions, laser power, and atmospheric effects while maintaining sensitivity to road wetness.
2Reliability
If multiple sensors and data sources are integrated to improve detection accuracy, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent makes the laser sensor multi-functional by enabling it to perform both traditional object detection and road weather condition detection. The same laser data used for navigation and obstacle avoidance is reprocessed to extract road surface intensity patterns, eliminating the need for separate dedicated sensors and reducing overall system complexity.
Solution Approach 2:
The system uses its own existing sensor data (laser returns) to detect road conditions without requiring external or additional sensors. By processing the laser intensity data through statistical analysis and comparison with expected dry-road patterns, the system achieves self-sufficient road weather detection.
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
Effectively identifies road weather conditions, enabling autonomous vehicles to make safe and adaptive driving decisions, improving safety and operational efficiency.
Implementation Method 1
laser data includes a plurality of laser data points, each data point of the plurality of laser data points having location and intensity information associated therewith
Data Source
AI summary
Aspects of the disclosure relate generally to detecting road weather conditions. Vehicle sensors including a laser, precipitation sensors, and/or camera may be used to detect information such as the brightness of the road, variations in the brightness of the road, brightness of the world, current precipitation, as well as the detected height of the road. Information received from other sources such as networked based weather information (forecasts, radar, precipitation reports, etc.) may also be considered. The combination of the received and detected information may be used to estimate the probability of precipitation such as water, snow or ice in the roadway. This information may then be used to maneuver an autonomous vehicle (for steering, accelerating, or braking) or identify dangerous situations.