Vision-Based Wet Road Detection Using Tire Tracks
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Solution Overview
Problem
Current vehicle systems detect precipitation on roads by monitoring wheel slip or introducing excitations, which can be delayed and may not accurately determine the presence of water until it affects vehicle operation, leading to stability issues.
Innovation Solution
A vision-based imaging system using a polarized image and edge filtering to identify tire tracks on the road surface, allowing for real-time detection of water without requiring vehicle or driver excitations, and a classifier to determine the presence of water based on tire track analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wheel slip monitoring is used to detect precipitation, then detection can be performed using existing vehicle sensors, but detection is delayed until precipitation already impacts vehicle operation
Solution Approach 1:
The system performs preliminary detection of precipitation by analyzing tire track patterns in images captured by external cameras before wheel slip occurs. The image processing system identifies characteristic patterns of tire tracks on wet surfaces, allowing the vehicle to detect precipitation conditions in advance and take preventive measures before traction is compromised.
Solution Approach 2:
The system introduces an intermediary detection method using external imaging devices and image pattern recognition as a mediator between the environment and vehicle sensors. Instead of relying directly on wheel slip signals from vehicle sensors, the system uses visual analysis of tire tracks as an intermediate indicator of precipitation conditions, enabling earlier and more accurate detection.
2Productivity
If vehicle excitations are introduced to detect precipitation, then detection can be initiated actively, but vehicle operation is disrupted and detection accuracy may be compromised
Solution Approach 1:
The system uses the vehicle's existing motion and tire rotation to create detectable tire track patterns on the road surface. No additional excitations or disruptions to vehicle operation are required - the natural rolling of tires during normal driving automatically generates the visual patterns needed for precipitation detection, making the system self-sufficient and operationally seamless.
Solution Approach 2:
The system replaces mechanical excitation methods (such as deliberate wheel slip induction or vibration) with an optical detection approach. Instead of mechanically disrupting vehicle operation to detect precipitation, the system uses external cameras to capture and analyze visual patterns of tire tracks, substituting mechanical detection with optical field-based detection.
3Loss of time
If external imaging devices are used to capture tire tracks, then precipitation can be detected before affecting vehicle operation, but system complexity increases
Solution Approach 1:
The system uses external imaging devices that serve multiple functions - they capture images for both precipitation detection and other vehicle functions such as surround-view monitoring or autonomous driving assistance. This multi-functionality reduces the need for dedicated specialized equipment, thereby limiting the increase in system complexity while still enabling advance precipitation detection.
Solution Approach 2:
The system creates a visual copy of the road surface and tire track patterns through external imaging, allowing analysis of precipitation conditions without physically interacting with the vehicle's mechanical systems. This optical copying approach simplifies the detection mechanism compared to mechanical sensors while enabling early detection through image pattern recognition.
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
Enables immediate and accurate detection of wet road surfaces, allowing for proactive mitigation strategies such as adjusting speed, applying gentle braking, or deactivating cruise control to maintain traction and prevent engine degradation.
Implementation Method 1
An image of a road surface is captured by an image capture device of the host vehicle
Implementation Method 2
The technique preferably captures an image that includes tire tracks left in the water on the road surface as the vehicle tire rotates along the road surface. The technique utilizes a polarized image of the captured scene and applies edge filtering to identify a line edge
Data Source
AI summary
A method of determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and an image is captured in a downward direction. A region of interest is identified in the captured image by a processor. The region of interest is in a region rearward of a tire of a host vehicle. The region of interest is representative of where a tire track as generated by the tire rotating on the road when the road surface is wet. A determination is made whether water is present in the region of interest as a function of identifying the tire track. A wet road surface signal is generated in response to the identification of water in the region of interest.