Vehicle Driving Control Triggering Data Storage Segmentation
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Solution Overview
Problem
Current vehicle systems face challenges in efficiently storing only necessary data during driving, leading to potential storage inefficiencies and user inconvenience, particularly in managing data transitions between manual and autonomous modes.
Innovation Solution
A method involving a processor-generated start and end trigger signal to determine when to store and terminate the storage of driving manipulation and navigation data, based on various situational triggers such as object detection, user input, and vehicle state information, allowing for targeted data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving manipulation data and navigation data are continuously stored during vehicle operation, then data availability for autonomous driving control is improved, but storage resource consumption increases
Solution Approach 1:
The system pre-identifies trigger conditions (start/end triggers) based on driving situations before actual data storage occurs. When a start trigger is detected, the system proactively begins storing manipulation and navigation data, and when an end trigger is detected, it stops storage. This preliminary identification of storage boundaries ensures data is available when needed while avoiding unnecessary continuous storage.
Solution Approach 2:
Instead of continuously storing all driving data, the system applies partial action by storing data only during specific segments identified by trigger conditions. This selective storage captures essential data for autonomous driving control while significantly reducing overall storage resource consumption compared to continuous storage approaches.
2Productivity
If trigger-based data storage is implemented to reduce storage consumption, then storage efficiency is improved, but data management complexity increases
Solution Approach 1:
The continuous driving process is segmented into distinct storage phases by dividing it into segments bounded by start triggers and end triggers. Each segment represents a specific driving situation where data storage is activated. This segmentation transforms the data management task from continuous monitoring to discrete event-based storage, improving efficiency while maintaining manageable complexity through clear phase boundaries.
Solution Approach 2:
The system employs feedback mechanisms where driving situation information continuously feeds back to the trigger determination unit. This feedback loop enables real-time detection of start and end triggers based on current driving conditions, allowing the system to dynamically adjust storage operations. The feedback ensures accurate identification of storage boundaries while automating the process to manage complexity.
Data Source
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AI summary
Disclosed herein is a method for driving a vehicle including generating, by at least one processor, a start trigger signal based on first driving situation information, storing, by the processor, driving manipulation data based on user input and navigation data generated based on the driving manipulation data in a memory when the start trigger signal is generated, generating, by the processor, an end trigger signal based on second driving situation information, and terminating the storing of the driving manipulation data and the navigation data when the end trigger signal is generated.