A Multi-model Approach for Video Data Retrieval in Autonomous Vehicle Development
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929629595435008 |
|---|---|
| author | Knapp, Jesper Moberg, Klas Jin, Yuchuan Sun, Simin Staron, Miroslaw |
| author_facet | Knapp, Jesper Moberg, Klas Jin, Yuchuan Sun, Simin Staron, Miroslaw |
| contents | Autonomous driving software generates enormous amounts of data every second, which software development organizations save for future analysis and testing in the form of logs. However, given the vast size of this data, locating specific scenarios within a collection of vehicle logs can be challenging. Writing the correct SQL queries to find these scenarios requires engineers to have a strong background in SQL and the specific databases in question, further complicating the search process. This paper presents and evaluates a pipeline that allows searching for specific scenarios in log collections using natural language descriptions instead of SQL. The generated descriptions were evaluated by engineers working with vehicle logs at the Zenseact on a scale from 1 to 5. Our approach achieved a mean score of 3.3, demonstrating the potential of using a multi-model architecture to improve the software development workflow. We also present an interface that can visualize the query process and visualize the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03580 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-model Approach for Video Data Retrieval in Autonomous Vehicle Development Knapp, Jesper Moberg, Klas Jin, Yuchuan Sun, Simin Staron, Miroslaw Software Engineering Artificial Intelligence Autonomous driving software generates enormous amounts of data every second, which software development organizations save for future analysis and testing in the form of logs. However, given the vast size of this data, locating specific scenarios within a collection of vehicle logs can be challenging. Writing the correct SQL queries to find these scenarios requires engineers to have a strong background in SQL and the specific databases in question, further complicating the search process. This paper presents and evaluates a pipeline that allows searching for specific scenarios in log collections using natural language descriptions instead of SQL. The generated descriptions were evaluated by engineers working with vehicle logs at the Zenseact on a scale from 1 to 5. Our approach achieved a mean score of 3.3, demonstrating the potential of using a multi-model architecture to improve the software development workflow. We also present an interface that can visualize the query process and visualize the results. |
| title | A Multi-model Approach for Video Data Retrieval in Autonomous Vehicle Development |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2410.03580 |