Road Defect Detection Using Vehicle Sensor Data
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Solution Overview
Problem
Current systems lack an effective method to detect road surface defects in real-time using sensor data from vehicles and provide timely alerts to nearby drivers and maintenance agencies.
Innovation Solution
A cloud detection system that collects and analyzes motion and position data from vehicles using sensors like GPS, accelerometers, and tire pressure monitors, correlating this data with vehicle profiles to identify road defects and trigger video recording or alerts, and communicates defect information to both drivers and repair crews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple vehicles is collected and centrally processed at a data center, then road defect detection accuracy is improved, but data transmission time and system complexity increase
Solution Approach 1:
The system performs preliminary processing of sensor data at the vehicle level before transmission to the data center. Each vehicle's processor pre-analyzes sensor data to identify potential road defects, extracting only relevant information for central processing. This preliminary action reduces the volume of data requiring transmission while maintaining detection accuracy, as the vehicle-level preprocessing filters out normal variations in sensor readings before central aggregation.
Solution Approach 2:
The detection system is segmented into distributed vehicle-level processing units and a central data center. Each vehicle independently processes its own sensor data to identify potential defects, then transmits only anomaly detections to the data center. This segmentation allows parallel processing across multiple vehicles, improving overall detection speed while reducing communication bandwidth requirements compared to centralized processing of all raw sensor data.
2Measurement precision
If vehicle-specific profiles are retrieved and used for detection, then detection accuracy is improved, but system complexity and data storage requirements increase
Solution Approach 1:
The vehicle profile database serves multiple functions: it stores baseline sensor characteristics for each vehicle, provides detection thresholds, and enables normalization of sensor readings. By making the profile database multi-functional, the system avoids creating separate storage systems for each detection parameter, reducing overall system complexity while maintaining the ability to perform vehicle-specific detection analysis.
Solution Approach 2:
Instead of storing complex, vehicle-specific detection algorithms for each vehicle type, the system creates simplified profile representations that capture essential vehicle characteristics. These profile copies contain normalized parameters and threshold values that replicate the effect of full vehicle-specific analysis without requiring the complete original vehicle data models, thereby reducing storage requirements and processing complexity.
3Reliability
If real-time detection and reporting is implemented, then driver safety is improved, but energy consumption and processing load increase
Solution Approach 1:
The system implements periodic sampling of sensor data at optimized intervals rather than continuous monitoring. Detection algorithms are triggered periodically or event-driven based on changes in sensor readings, allowing the vehicle's processing system to enter low-power states between detection cycles. This periodic operation maintains real-time safety monitoring capability while significantly reducing average energy consumption compared to continuous real-time processing.
Solution Approach 2:
The vehicle's existing sensor systems and processors are utilized for road defect detection rather than adding dedicated detection hardware. The vehicle's normal operational sensors (accelerometers, GPS, tire pressure monitors) serve dual purposes: vehicle operation monitoring and road defect detection. This self-service approach eliminates the need for additional energy-consuming detection equipment while maintaining detection capability.
Data Source
AI summary
A method of detecting and identifying road surface defects is provided. Motion and position information is received from a plurality of vehicles. A profile is retrieved for a particular vehicle from a database of vehicle profiles by using an identifier of the particular vehicle. One or more criteria are identified for detecting a particular type of road surface defect based on the retrieved profile of the particular vehicle. Upon determining that the received motion and position data satisfies the identified criteria, a detection of a road surface defect of the particular type and a location associated with the detected road surface defect based on the received position information is reported.