Vehicle Black Box Data Retention for Critical Event Storage
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Solution Overview
Problem
Intelligent driving vehicles pose unique challenges in black box data management due to changes in application scenarios, driver habits, and system interactions, requiring a more effective method to record and store data for accurate accident responsibility determination and improved safety.
Innovation Solution
A black box data management method that classifies data based on trigger events, such as driving mode switching and risk boundary events, and stores it differently in local and cloud storage, ensuring long-term retention of critical data while periodically deleting non-essential data to optimize storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all black box data is stored permanently in local storage, then data completeness for responsibility determination is improved, but storage space is quickly exhausted and system cost increases
Solution Approach 1:
The patent segments black box data into three categories based on importance: responsibility determination data (critical for accident analysis), assistant determination data (supporting evidence), and risk data (general monitoring). This segmentation enables differential storage strategies where critical data is preserved long-term while less critical data is managed with shorter retention or cloud-only storage, resolving the contradiction between data completeness and storage space consumption
Solution Approach 2:
The patent applies local quality by implementing different storage policies for different data types within the same system. Responsibility determination data receives premium local storage with long-term retention, while risk data may be stored in cloud only or with shorter local retention. This differentiated approach ensures critical data availability while optimizing overall storage resource utilization
2Quantity of substance
If critical data is retained long-term and non-essential data is deleted, then storage space efficiency is improved, but data accuracy for responsibility determination may be compromised
Solution Approach 1:
The patent implements preliminary action by pre-classifying data into categories (responsibility determination, assistant determination, risk data) before storage based on predefined criteria. This advance classification ensures that critical data is identified and preserved with appropriate retention policies before any deletion occurs, preventing accidental loss of accuracy-critical data while enabling efficient space management for non-essential data
Solution Approach 2:
The system incorporates feedback mechanisms where the detection controller continuously monitors driving events and dynamically adjusts data retention decisions. When critical events are detected (collisions, emergency braking, mode switching), the system automatically ensures related data is preserved. This feedback loop maintains data accuracy by adapting retention policies based on actual driving conditions and event significance
3Productivity
If multiple types of data are stored with different retention periods, then storage resource utilization is improved, but data management complexity increases
Solution Approach 1:
The patent implements a universal data management architecture where the black box device performs multiple functions: data collection, classification, storage, and deletion. The detection controller also serves dual roles in both detecting driving events and triggering appropriate data retention actions. This multi-functionality reduces the need for separate specialized systems, managing complexity while enabling differentiated storage strategies across data types
Data Source
AI summary
Provided is a black box data management method and apparatus for an intelligent driving vehicle. A black box device first obtains black box data based on a black box trigger event, wherein the black box device is configured to manage the black box data in the intelligent driving vehicle. The black box device evaluates a storage level of the black box data based on an event type of the black box trigger event and a data type of the black box data. The black box device stores the black box data based on the storage level and according to a preset rule.