Vehicle Construction Area Detection With Hazard Prediction Alerts
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Solution Overview
Problem
Construction areas pose safety risks due to unpredictable and dynamic environments, requiring advanced systems to autonomously detect and alert drivers of potential hazards, which existing technologies have not adequately addressed.
Innovation Solution
A method and system that utilizes machine learning to generate and cluster signatures of construction area elements and scenarios, determining identifiers and behavioral predictors to autonomously detect construction area presence and predict dangerous events, triggering alerts and adaptive driving responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to generate and cluster signatures of construction area elements, then measurement precision of construction area detection is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary clustering of construction area element signatures during offline training phases, creating pre-computed clusters that can be quickly referenced during runtime. This preliminary action reduces the computational burden during real-time operation while maintaining high detection accuracy.
Solution Approach 2:
The system creates simplified representations (signatures) of construction area elements that capture essential characteristics without requiring full complexity of the original scenes. These signature copies enable efficient comparison and classification while preserving detection accuracy.
2Reliability
If behavioral predictors are used to predict dangerous events, then reliability of safety prediction is improved, but loss of time for processing increases
Solution Approach 1:
Behavioral predictors are trained offline using historical data to establish patterns of dangerous events. This preliminary training creates pre-computed models that can quickly evaluate current situations without requiring extensive real-time processing, thus maintaining high reliability while reducing processing time.
Solution Approach 2:
The system focuses on predicting only the most critical dangerous events rather than analyzing all possible scenarios. By concentrating computational resources on high-risk predictions, the system achieves reliable safety assessment with reduced processing time for less critical situations.
3Measurement precision
If construction area indicators are processed to determine vehicle location, then measurement precision of location detection is improved, but use of energy for processing increases
Solution Approach 1:
The system extracts only the most relevant features from construction area indicators that are sufficient for accurate location determination. By selecting and processing only essential indicators rather than all available data, the system maintains high location accuracy while reducing energy consumption.
Solution Approach 2:
The system processes construction area indicators at different levels of detail depending on the situation. For routine location tracking, simplified processing is used, while more intensive processing is activated only when higher precision is needed or uncertainty is detected, optimizing the balance between accuracy and energy use.
Data Source
AI summary
A method for generating at least one construction area indicator, the method may include receiving by a vehicle computerized system, construction area indicators; wherein a construction area indicator is indicative of a construction area element; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more construction area indicators of the construction area indicators; wherein the construction area element is selected out of (i) a construction area object and (ii) a construction area situation; autonomously determining, when finding at least one of the one or more construction area identifiers, that the vehicle is driving towards a construction area or is within the construction area; and generating an alert when determining that the vehicle is driving towards the construction area or is within the construction area.