Summary of Inherent Diverse Redundant Safety Mechanisms For Ai-based Software Elements in Automotive Applications, by Mandar Pitale et al.
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications
by Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research investigates the role and challenges of Artificial Intelligence (AI) algorithms in autonomous driving systems. Specifically, it explores how AI-based software elements execute real-time critical functions in complex environments, handling tasks like multi-modal perception, cognition, and decision-making. A key concern is the ability of AI models to generalize beyond their initial training data, as they frequently encounter inputs not represented in their training or validation data. To mitigate risks associated with overconfident AI models in safety-critical applications, methods for training AI models that maintain performance without overconfidence are proposed. This includes implementing certainty reporting architectures and ensuring diverse training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Artificial Intelligence (AI) helps autonomous cars make decisions. It talks about the challenges of using AI in self-driving cars, like handling different types of data and making quick decisions. The big question is: can AI models learn from what they know and apply it to new situations? To keep AI systems safe, researchers propose new ways to train them so they don’t get too confident and make mistakes. |
Keywords
* Artificial intelligence * Multi modal
