Autonomous Driving Control Using External Data for Part Diagnosis
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Solution Overview
Problem
Autonomous vehicles face limitations in deriving accurate failure diagnosis results due to reliance on autonomously collected data, making it difficult to consider parameters like model, production time, manufacturer, and usage history, leading to inadequate adaptive diagnosis.
Innovation Solution
An apparatus comprising a sensor device, storage, and a controller that collects real-time vehicle data, receives datasets from external devices, selects signals based on vehicle information, calculates reference prediction values, and compares them with real-time data to determine part states and potential failures, incorporating parameters like production time and usage history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autonomous vehicle performs part diagnosis using only autonomously collected information, then system complexity is reduced, but measurement precision of failure diagnosis deteriorates
Solution Approach 1:
The patent introduces an external electronic device as an intermediary that stores and provides reference information (production time, manufacturer, model, usage history) to the autonomous vehicle. This mediator enables the vehicle to access comprehensive diagnostic data without internally storing all reference information, thus maintaining low system complexity while achieving high measurement precision in failure diagnosis.
Solution Approach 2:
The patent transitions from a single-dimension autonomous collection model to a multi-dimension diagnostic approach by incorporating temporal (production time, usage history), manufacturing (manufacturer, model), and operational (usage pattern, mileage) dimensions. This dimensional expansion enables comprehensive part diagnosis without proportionally increasing system complexity.
2Measurement precision
If autonomous vehicle collects and processes comprehensive vehicle information and usage history, then failure diagnosis accuracy is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent implements preliminary action by having the external electronic device pre-store comprehensive reference information (production time, manufacturer, model, usage history) in an organized manner before diagnostic needs arise. When diagnosis is required, the autonomous vehicle can quickly retrieve relevant pre-prepared data without performing time-consuming data collection and processing, thus achieving high diagnostic accuracy with minimal time loss.
3Adaptability or versatility
If autonomous vehicle uses multiple parameters (model, production time, manufacturer, usage history) for diagnosis, then adaptability of diagnosis system is improved, but device complexity increases
Solution Approach 1:
The external electronic device serves as an intermediary that manages the complexity of storing and organizing multiple diagnostic parameters (model, production time, manufacturer, usage history). The autonomous vehicle interacts with this mediator through simple queries, enabling adaptable multi-parameter diagnosis without the vehicle's system complexity increasing proportionally to the number of parameters.
Data Source
AI summary
An apparatus collects real-time driving information and real-time state information of an autonomous vehicle, receives a dataset from an external electronic device, selects a signal for collecting pieces of information of a plurality of parts, based on at least a portion of the dataset, identifies at least one policy associated with the selected signal, selects a first policy among the at least one policy, based on the at least a portion of the dataset, collects the signal, based on the first selected first policy, calculates a reference prediction value, based on the at least a portion of the dataset, compares the real-time driving information and the real-time state information with the calculated reference prediction value, and identifies states of the plurality of parts using the compared result.