Summary of Human-in-the-loop Machine Learning For Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities, by Yousef Emami et al.
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
by Yousef Emami, Luis Almeida, Kai Li, Wei Ni, Zhu Han
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper reviews Human-In-The-Loop Machine Learning (HITL-ML) for Autonomous Vehicles (AVs), which leverages humans’ creativity, ethical power, and emotional intelligence to improve ML effectiveness. The authors focus on Curriculum Learning (CL), Human-In-The-Loop Reinforcement Learning (HITL-RL), Active Learning (AL), and ethical principles. CL trains ML models by starting with simple tasks and gradually progressing to more difficult ones. HITL-RL enhances the RL process through techniques like reward shaping, action injection, and interactive learning. AL streamlines annotation by targeting specific instances that need labeling with human oversight. The paper also discusses the importance of embedding ethical principles in AVs to align their behavior with societal values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we can make self-driving cars better by using people’s brains along with machine learning. It explains different ways to do this, like teaching machines to learn gradually or getting humans to help them make decisions. The authors also emphasize the importance of making sure these cars behave in a way that aligns with our values and norms. |
Keywords
» Artificial intelligence » Active learning » Curriculum learning » Embedding » Machine learning » Reinforcement learning
