AV Scenario Database for Searchable Safety-Critical Auditing
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Solution Overview
Problem
The challenge in autonomous vehicle (AV) technology is the time and effort-intensive process of identifying and auditing safety-critical scenarios from vast datasets generated by AV sensors, which hinders researchers' ability to focus on algorithm development rather than data analysis.
Innovation Solution
A system and method called AVDSAS (Autonomous Vehicle Data Searching and Auditing System) that acquires, processes, and stores AV data in a standardized format, allowing for efficient querying and extraction of scenarios using a scenario database, with components deployable in cloud computing or as edge devices, enabling users to search, audit, and generate performance reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual annotation methods are used to identify scenarios from AV data, then measurement precision can be achieved, but time consumption and effort increase significantly
Solution Approach 1:
The system performs preliminary processing of AV data by automatically generating scenario annotations and storing them in a scenario database before researchers need them. This pre-processing includes extracting scenario information, generating scenario IDs, and organizing data structures, so that when researchers query for scenarios, the work is already done and they only need to retrieve pre-annotated results.
Solution Approach 2:
The patent introduces an intermediary automated processing system that acts as a mediator between raw AV data and researcher analysis. This intermediary system includes components for automatic scenario detection, annotation generation, and database management, which handle the time-consuming manual tasks while providing structured, queryable scenario data to researchers.
2Ease of operation
If automated annotation algorithms are used to process AV data, then effort is reduced compared to manual methods, but time consumption remains high when analyzing multiple events in the same data file
Solution Approach 1:
The system performs preliminary processing of AV data by automatically generating scenario annotations and storing them in a scenario database before researchers need them. This pre-processing includes extracting scenario information, generating scenario IDs, and organizing data structures, so that when researchers query for scenarios, the work is already done and they only need to retrieve pre-annotated results.
Solution Approach 2:
The patent creates a copy of the essential scenario information in a structured database format that can be efficiently queried and retrieved. Instead of repeatedly processing the entire raw AV data files, the system stores extracted scenario data (including multiple events from the same file) in an accessible database, allowing rapid retrieval and analysis without re-processing the original large-scale sensor data.
3Reliability
If researchers manually analyze huge datasets to identify safety-critical scenarios, then comprehensive safety assessment can be achieved, but productivity decreases due to overwhelming data volume
Solution Approach 1:
The patent segments the huge AV dataset into manageable scenario units with unique identifiers. Each scenario is extracted and stored as a discrete entity in the database, allowing researchers to work with individual scenarios or groups of scenarios rather than overwhelming raw data. This segmentation enables systematic analysis while maintaining comprehensive safety assessment coverage.
Solution Approach 2:
The patent introduces an intermediary automated processing system that acts as a mediator between raw AV data and researcher analysis. This intermediary system includes components for automatic scenario detection, annotation generation, and database management, which handle the time-consuming manual tasks while providing structured, queryable scenario data to researchers.
Data Source
AI summary
An autonomous vehicle data searching and auditing system, is provided. The AVDSAS includes a scenario database storing therein autonomous vehicle data associated with AV(s). The AVDSAS has a scenario extraction module that is in operable communication with the scenario database. The scenario extraction module extracts scenario data from the AV data and stores the scenario data into the scenario database, wherein the scenario data includes AV parameter(s), object(s), and operational design domain element(s) associated with the AV(s). The AV data stored in the scenario database is searchable based on a query. The scenario extraction model generates scenario(s) using the scenario data from the scenario database based on the query.