Human-in-the-Loop ML for Safer Autonomous Vehicles Explored

A new arXiv paper explores the integration of Human-in-the-Loop Machine Learning (HITL-ML) techniques to enhance the safety and ethical considerations of autonomous vehicles (AVs). The paper details how human input, through methods like validation, annotation, and preference feedback, can address challenges in perception, prediction, and decision-making, particularly in complex driving scenarios. It covers various HITL-ML approaches including Curriculum Learning, Reinforcement Learning, Large Language Models, and Active Learning, while also emphasizing the importance of transparency, accountability, and reliability in human oversight for AV systems. AI

IMPACT Enhances safety and ethical considerations in autonomous vehicle development by integrating human oversight into machine learning processes.

RANK_REASON The cluster contains a research paper published on arXiv detailing principles and methods for human-in-the-loop machine learning in autonomous vehicles. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• active learning
• arXiv
• Autonomous Vehicles
• Curriculum Learning
• Human-in-the-Loop Large Language Models
• Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities
• Yousef Emamipoor

• paper
• safety
• product

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