Road Surface Severity Estimation for Off-Road Mode Switching
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Solution Overview
Problem
Existing road surface condition estimation technologies fail to accurately assess the severity of road surfaces, particularly in off-road environments, which can lead to vehicles getting stuck due to varying road surface characteristics such as depth and resistance.
Innovation Solution
A road surface condition estimation apparatus utilizing a deep learning network to estimate road surface severity based on travel information, including engine torque, speed, and acceleration, with a post-processor applying exponential moving averages to stabilize estimates, and distinguishing between shallow and deep road surfaces using predefined reference scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a vehicle travels on off-road surfaces, then the vehicle can access diverse terrains and destinations, but the vehicle is more likely to get stuck due to varying road surface conditions such as depth and resistance
Solution Approach 1:
The system performs preliminary estimation of road surface severity before the vehicle encounters difficult terrain. By continuously monitoring travel information and estimating road surface conditions in advance, the system prepares optimal travel modes beforehand, preventing the vehicle from getting stuck on challenging off-road surfaces.
Solution Approach 2:
The system establishes a feedback loop where travel information from sensors is continuously fed into the road surface severity estimation model. The estimated severity scores are then used to adjust travel modes, and this process continues dynamically as the vehicle moves through different terrains, enabling real-time adaptation to changing road conditions.
2Loss of information
If existing road surface classification apparatuses are used, then road surface types can be identified, but the severity of each road surface condition cannot be verified
Solution Approach 1:
The system transforms the road surface assessment from simple type classification to a continuous severity scoring system. By changing the output parameter from discrete categories to a continuous score representing immersion depth and resistance, the system provides precise measurement of road surface severity while maintaining type identification capabilities.
Solution Approach 2:
The system adds a new dimension to road surface assessment by introducing severity scoring based on multiple parameters including immersion depth and resistance. This dimensional expansion allows the system to evaluate not just what type of road surface the vehicle is on, but also how severe the conditions are, providing comprehensive verification of road surface characteristics.
Data Source
AI summary
A road surface condition estimation apparatus includes a storage unit configured to store a road surface condition estimation model, and a road surface severity estimator configured to estimate, based on travel information, severity of a condition of a road surface on which a vehicle is travelling using the road surface condition estimation model.