Key Safety Design Overview in AI-driven Autonomous Vehicles
Bewaard in:
| Gepubliceerd in: | arXiv.org (Dec 12, 2024), p. n/a |
|---|---|
| Hoofdauteur: | |
| Andere auteurs: | |
| Gepubliceerd in: |
Cornell University Library, arXiv.org
|
| Onderwerpen: | |
| Online toegang: | Citation/Abstract Full text outside of ProQuest |
| Tags: |
Geen labels, Wees de eerste die dit record labelt!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3144199766 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3144199766 | ||
| 045 | 0 | |b d20241212 | |
| 100 | 1 | |a Vyas, Vikas | |
| 245 | 1 | |a Key Safety Design Overview in AI-driven Autonomous Vehicles | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 12, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a With the increasing presence of autonomous SAE level 3 and level 4, which incorporate artificial intelligence software, along with the complex technical challenges they present, it is essential to maintain a high level of functional safety and robust software design. This paper explores the necessary safety architecture and systematic approach for automotive software and hardware, including fail soft handling of automotive safety integrity level (ASIL) D (highest level of safety integrity), integration of artificial intelligence (AI), and machine learning (ML) in automotive safety architecture. By addressing the unique challenges presented by increasing AI-based automotive software, we proposed various techniques, such as mitigation strategies and safety failure analysis, to ensure the safety and reliability of automotive software, as well as the role of AI in software reliability throughout the data lifecycle. Index Terms Safety Design, Automotive Software, Performance Evaluation, Advanced Driver Assistance Systems (ADAS) Applications, Automotive Software Systems, Electronic Control Units. | |
| 653 | |a Software reliability | ||
| 653 | |a Computer program integrity | ||
| 653 | |a Advanced driver assistance systems | ||
| 653 | |a Failure analysis | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Electronic control | ||
| 653 | |a Automotive electronics | ||
| 653 | |a Control equipment | ||
| 700 | 1 | |a Xu, Zheyuan | |
| 773 | 0 | |t arXiv.org |g (Dec 12, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3144199766/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.08862 |