Automated Vehicle Diagnostics Using Sensor Segmentation
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Solution Overview
Problem
Current vehicle maintenance schedules are imprecise, leading to maintenance being performed too soon or too late, and coordinating maintenance between vehicles and repair facilities is an administrative burden, lacking efficient diagnostics and scheduling solutions.
Innovation Solution
Implement automated vehicle diagnostics and maintenance systems that utilize sensors to monitor vehicle performance, aggregate data, and schedule maintenance based on determined issues, assigning technicians and locations optimally, and managing inventory and technician skills dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated diagnostics systems are implemented, then measurement precision of vehicle status is improved, but device complexity increases
Solution Approach 1:
The diagnostics system is segmented into multiple independent sensor modules, each monitoring specific vehicle parameters (engine temperature, oil pressure, battery status, etc.). This modular approach improves measurement precision for each parameter while managing overall system complexity through distributed functionality.
Solution Approach 2:
The system employs universal sensor platforms that can monitor multiple vehicle parameters and serve multiple diagnostic functions. These multi-functional sensors reduce the total number of specialized components needed, balancing measurement precision with system complexity.
2Productivity
If maintenance scheduling is based on actual vehicle condition data, then productivity is improved, but loss of time for data processing increases
Solution Approach 1:
The system continuously collects and pre-processes vehicle condition data in the background during normal operation. By preparing diagnostic information in advance and maintaining ready-to-analyze data buffers, the system enables rapid maintenance scheduling decisions without significant processing delays when maintenance is needed.
Solution Approach 2:
The system implements real-time feedback loops where sensor data continuously informs vehicle status assessments and maintenance recommendations. This ongoing feedback mechanism processes data incrementally rather than in large batches, reducing overall processing time while maintaining high maintenance productivity.
3Reliability
If comprehensive sensor monitoring is deployed, then reliability of vehicle status assessment is improved, but use of energy increases
Solution Approach 1:
The sensor monitoring system operates periodically rather than continuously, with sensors activated at scheduled intervals based on vehicle operation cycles. This periodic sampling maintains reliable vehicle status assessment by capturing key parameters at meaningful intervals while significantly reducing overall energy consumption compared to continuous monitoring.
Data Source
AI summary
Systems, methods, and apparatuses described herein are directed to automated vehicle diagnostics and maintenance. For example, vehicles can include sensors monitoring vehicle components, for perceiving objects and obstacles in an environment, and for navigating the vehicle to a destination. Data from these and other sensors can be leveraged to track a performance of the vehicle over time to determine a state of vehicle components, and/or changes to acceleration/deceleration and steering behavior of the vehicle over time. As issues for servicing are determined, the methods, apparatuses, and systems can include automated scheduling of vehicle maintenance. For example, based on a determination of the potential issues based on sensor data and/or user indications, vehicle maintenance can be scheduled to be performed in the field or at a service center. Technicians can be assigned to perform vehicle maintenance based on the servicing issue and/or on capabilities of the technician.