Road Condition Determination via Predicted Route Friction Analysis
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Solution Overview
Problem
Existing road surface condition determination methods struggle to accurately estimate the road surface friction coefficient for vehicle travel, especially when a vehicle is on a high μ road, leading to insufficient travel performance and safety issues when controlling as if on a low μ road.
Innovation Solution
A method that predicts the vehicle's route and determines the road surface condition using a camera capable of capturing ultraviolet, infrared, and temperature distribution images, allowing for accurate estimation of the road surface friction coefficient and optimal driving force distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If road surface condition is determined based on camera information without route prediction, then the determination process is simple, but the accuracy of road surface friction coefficient estimation for vehicle travel is insufficient
Solution Approach 1:
The system performs preliminary route prediction before determining road surface conditions. By predicting the vehicle's future trajectory in advance, the system can identify which road segments the vehicle will actually contact, then focus road surface condition determination on those specific segments. This preliminary action resolves the contradiction by adding a preparatory step that enables accurate friction coefficient estimation without unnecessarily complicating the overall system.
2Reliability
If control is performed by assessing as low μ road when low μ road and high μ road are distributed ahead, then vehicle safety is ensured, but travel performance on high μ road regions is insufficient
Solution Approach 1:
The system applies local quality by determining road surface conditions separately for different spatial regions along the predicted route. Instead of uniformly assessing the entire ahead region, the system identifies specific road segments where the vehicle will travel and determines friction coefficients for those localized areas. This enables the control system to optimize for each local condition, ensuring safety on low μ segments while maximizing performance on high μ segments.
Solution Approach 2:
The system segments the road ahead into multiple determination areas based on the predicted vehicle route. By dividing the continuous road surface into discrete segments and evaluating friction coefficients for each segment independently, the system can apply appropriate control strategies for each local condition. This segmentation resolves the contradiction by allowing differentiated control that balances safety and performance across varying road conditions.
3Ease of operation
If road surface condition determination is performed without considering vehicle route, then the system is simple to operate, but the determined condition does not accurately reflect the condition where wheels contact the ground
Solution Approach 1:
The system employs self-service by automatically obtaining vehicle route information from existing vehicle sensors and control systems. Rather than requiring manual input or complex external data sources, the system leverages the vehicle's own navigation and sensor data to predict its trajectory. This automatic self-service approach maintains ease of operation while enabling accurate route-based road surface condition determination.
Data Source
AI summary
In the present invention, when a road surface condition is determined based on information acquired by a camera installed in a vehicle, a route of a host vehicle is predicted and the is determined. A road surface condition determination method and device determines a road surface condition of a predicted route based on information acquired by a camera installed in a host vehicle. A controller predicts a route of the host vehicle by determining a road surface friction coefficient of the predicted route based on information acquired by the camera. The determining of the road surface friction coefficient of the predicted route includes: dividing an ahead-of-vehicle image acquired by the camera in a left-right direction and determining a road surface condition for each of the determination areas, and determining the road surface friction coefficient in the determination areas through which the predicted route will pass.