Roadway Hazard Detection Using Vehicle Sensors and ML Alerts
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Solution Overview
Problem
Current vehicular systems and government agencies face challenges in detecting and addressing hazardous roadway conditions in real-time, leading to increased vehicular damage, injuries, and fatalities due to delayed remediation of hazards like potholes and other obstacles.
Innovation Solution
Implementing a machine-learning based roadway hazard detection and notification system that uses sensors and cameras to identify hazards, transmit data to other vehicles and authorities, and trigger remedial actions, such as altering driving paths or notifying maintenance entities, to enhance detection and remediation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to detect roadway hazards, then detection accuracy is improved, but response time is delayed due to processing and communication overhead
Solution Approach 1:
The system performs preliminary detection and classification of roadway hazards using sensors and machine learning models before critical situations arise. By continuously monitoring and pre-processing data from LiDAR, cameras, and other sensors, the system prepares hazard information in advance, allowing downstream systems to react faster without waiting for full processing cycles.
Solution Approach 2:
The patent introduces a communication system as an intermediary that transmits hazard information between vehicles and to central authorities. This intermediary layer enables parallel processing where vehicles share real-time hazard data through V2V communication, reducing the effective response time for individual vehicles while maintaining high detection accuracy through collective sensing.
2Reliability
If real-time hazard detection and communication systems are implemented, then road safety is improved, but system complexity increases
Solution Approach 1:
The system employs multi-functional sensors and processing units that perform multiple tasks. For example, LiDAR and camera systems not only detect hazards but also map roadway geometry, identify traffic patterns, and support various detection algorithms. This universal approach reduces the need for dedicated hardware for each function, managing complexity while enhancing safety.
Solution Approach 2:
The patent implements feedback loops where detection results are communicated back to the vehicle control systems and central authorities. This feedback mechanism enables continuous improvement of the system's reliability through learning from past detections and adjustments, while the standardized feedback protocols help manage communication complexity.
3Adaptability or versatility
If comprehensive sensor deployment is used to detect all roadway conditions, then detection coverage is improved, but cost and energy consumption increase
Solution Approach 1:
The system implements partial sensing strategies where not all sensors operate at full capacity simultaneously. Based on environmental conditions, vehicle speed, and detected hazard levels, the system dynamically activates only the necessary sensors and processing algorithms, reducing energy consumption while maintaining adequate detection coverage for current conditions.
Solution Approach 2:
The patent employs dynamic sensor management where the sensing configuration adapts in real-time to changing conditions. For example, sensors are activated or deactivated based on lighting conditions, weather, vehicle speed, and detected hazard types. This dynamic approach ensures comprehensive coverage when needed while conserving energy during normal driving conditions.
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
The system enables more accurate and timely detection of roadway hazards, reducing vehicular damage and injuries by immediately notifying drivers and maintenance entities, thereby improving road safety and maintenance efficiency.
Implementation Method 1
The roadway conditions may be detected using a variety of sensors (e.g., LiDAR) and cameras.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for detecting and remediating roadway hazards. In example aspects, a machine learning model is trained on a dataset related to roadway items. Input data may then be collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer may extract roadway features (physical and non-physical) and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a risk value may be generated. The risk value may be compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action may subsequently be triggered, e.g., notifying other vehicles on the roadway, notifying other devices of drivers on the roadway, and/or notifying interested third parties (e.g., government agencies and/or private entities responsible for maintaining a roadway), to decrease the frequency of vehicles colliding with roadway hazards and to increase the efficiency of remediating the identified roadway hazards.