Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.09233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915149733953536 |
|---|---|
| author | Kimbrell, Keegan |
| author_facet | Kimbrell, Keegan |
| contents | Autonomous Vehicle (AV) systems have been developed with a strong reliance on machine learning techniques. While machine learning approaches, such as deep learning, are extremely effective at tasks that involve observation and classification, they struggle when it comes to performing higher level reasoning about situations on the road. This research involves incorporating commonsense reasoning models that use image data to improve AV systems. This will allow AV systems to perform more accurate reasoning while also making them more adjustable, explainable, and ethical. This paper will discuss the findings so far and motivate its direction going forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Commonsense Reasoning-Aided Autonomous Vehicle Systems Kimbrell, Keegan Artificial Intelligence Autonomous Vehicle (AV) systems have been developed with a strong reliance on machine learning techniques. While machine learning approaches, such as deep learning, are extremely effective at tasks that involve observation and classification, they struggle when it comes to performing higher level reasoning about situations on the road. This research involves incorporating commonsense reasoning models that use image data to improve AV systems. This will allow AV systems to perform more accurate reasoning while also making them more adjustable, explainable, and ethical. This paper will discuss the findings so far and motivate its direction going forward. |
| title | Commonsense Reasoning-Aided Autonomous Vehicle Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09233 |