Vehicle Sensor Fusion for Reliable Black Ice Recognition
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Solution Overview
Problem
Existing methods for road condition recognition, particularly for conditions like black ice, are not sufficiently reliable and require costly, time-consuming manual labeling of training data, and existing sensors struggle to accurately differentiate between various road conditions under varying light conditions.
Innovation Solution
Utilize surroundings-capturing sensors in vehicles to gather data, combined with a precise reference sensor that determines road conditions, and employ a machine learning system, specifically a neural network, to learn and predict road conditions using supervised learning and data from a data-driven ecosystem (DDE) to ensure robust recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training data is used to train road condition recognition systems, then the system can learn to recognize road conditions, but the process becomes costly and time-consuming
Solution Approach 1:
The system uses automated algorithms to label training data based on sensor inputs (camera images, radar data, weather information) without requiring manual human annotation. The machine learning model learns to identify road conditions such as ice, snow, wet surfaces, and dry surfaces autonomously, eliminating the need for costly and time-consuming manual labeling while maintaining recognition accuracy
Solution Approach 2:
The patent introduces an intermediate processing layer that automatically generates labels by combining multiple sensor data sources. This intermediary system processes raw sensor data through algorithms that infer road conditions, creating training labels that are both accurate and generated efficiently without direct human intervention
2Adaptability or versatility
If existing sensors are used to detect road conditions, then the system can operate with available hardware, but the system cannot reliably differentiate between various road conditions under varying light conditions
Solution Approach 1:
The system merges data from multiple sensor types including cameras, radar, LIDAR, and weather sensors to create a comprehensive view of road conditions. By combining these diverse data sources, the system achieves reliable differentiation between various road conditions (ice, snow, wet, dry) across different lighting conditions, overcoming the limitations of individual sensors
Solution Approach 2:
The patent adds temporal and environmental dimensions to road condition detection by incorporating weather data, time of day, and historical road condition information. This multi-dimensional approach allows the system to distinguish between similar-looking conditions (such as dark ice versus shadowed dry road) by considering additional contextual factors beyond just visual appearance
3Productivity
If driving tests for autonomous vehicles are conducted in sunny areas, then the tests can proceed without snow and ice interference, but the system cannot learn to recognize or respond to winter road conditions
Solution Approach 1:
The system performs preliminary learning of winter road conditions through simulated training data and transfer learning from other sources before actual deployment. By pre-training the machine learning model with synthetic winter condition data and data from diverse environments, the system acquires winter road condition recognition capabilities ahead of time, allowing efficient testing in sunny areas while still being prepared for winter conditions
Solution Approach 2:
The patent uses synthetic data generation and transfer learning to create virtual representations of winter road conditions. By copying and adapting data from various sources to simulate winter scenarios, the system learns to recognize ice, snow, and cold-weather road conditions without requiring physical tests in snowy environments, thus maintaining test productivity while achieving winter condition adaptability
Data Source
AI summary
The present disclosure relates to a method and system for monitoring the road condition by a machine learning system and to a method for training the machine learning system. The methods include: providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle (2) as training input data X; providing or acquiring data which characterize the road condition by means of a reference sensor fitted in or on the vehicle as training target values, and training the machine learning system. Training data, which include training input data X and training target values corresponding to these training input data X, are provided. The training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.