Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Yousef Emami, Mohammadhossein Homaei, Miguel Guti\'errez Gait\'an, Luis Almeida, Kai Li, Hui Huang, Zhu Han
Why It Matters
What makes this one worth your time
Understanding how to effectively incorporate human oversight in autonomous vehicle systems is crucial for improving safety, reliability, and ethical compliance in real-world applications.
A survey on integrating human input into machine learning for safer and more ethical autonomous vehicles.
Summary
The paper provides a tutorial survey on Human-In-The-Loop Machine Learning (HITL-ML) for autonomous vehicles, focusing on various methodologies such as Curriculum Learning, HITL Reinforcement Learning, HITL Large Language Models, Active Learning, and ethical principles. It reviews existing methods and discusses their application in improving safety and ethical standards in autonomous vehicle systems.
Key contributions
- A comprehensive review of Human-In-The-Loop methodologies for autonomous vehicles.
- Discussion of ethical principles as technical requirements for autonomous systems.
- Exploration of Active Learning for improving perception and anomaly detection in AVs.
Notable insights
- The integration of human feedback in reinforcement learning can enhance learning efficiency and policy safety.
- Curriculum Learning structures training from simple to complex tasks, which can be beneficial for autonomous vehicle navigation and planning.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2408.12548v3 Announce Type: replace Abstract: Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However, achieving full autonomy in cluttered and complex scenarios, such as intricate intersections, diverse scenes, varied trajectories, and complex missions, remains challenging; moreover, data labeling is still a major bottleneck. These limitations motivate Human-in-the-Loop Machine Learning (HITL-ML), in which human input is incorporated through validation, annotation, task organization, reward design, action correction, preference feedback, and supervisory intervention. To advance safe and ethical autonomy, this paper presents a tutorial survey of HITL-ML for AVs, focusing on Curriculum Learning (CL), Human-in-the-Loop Reinforcement Learning (HITL-RL), Human-in-the-Loop Large Language Models (HITL-LLMs), Active Learning (AL), and ethical principles. We first review CL methods that structure training from simple to complex tasks, covering navigation, path planning, obstacle avoidance, data collection, landing, intersection handling, motion planning, and UAV swarm coordination. We then examine HITL-RL through reward shaping, action injection, demonstrations, preference-based feedback, and interactive learning, emphasizing improved learning efficiency, safer policy exploration, and real-time intervention. Next, we review HITL-LLM through collaboration and oversight and specify key challenges. After that, we discuss AL for perception, anomaly detection, semantic mapping, object detection, vehicle recognition, and security-related classification. Ethical principles are reviewed as technical requirements for transparency, accountability, human oversight, safety, security, regulatory compliance, and reliability of human input.