A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System
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_ | 1866929644401328128 |
|---|---|
| author | Aslam, Iqra Buragohain, Abhishek Bamal, Daniel Aniculaesei, Adina Zhang, Meng Rausch, Andreas |
| author_facet | Aslam, Iqra Buragohain, Abhishek Bamal, Daniel Aniculaesei, Adina Zhang, Meng Rausch, Andreas |
| contents | Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16762 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System Aslam, Iqra Buragohain, Abhishek Bamal, Daniel Aniculaesei, Adina Zhang, Meng Rausch, Andreas Robotics Artificial Intelligence Software Engineering Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications. |
| title | A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System |
| topic | Robotics Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2412.16762 |