Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities

Rapid advances in Machine Learning (ML) have triggered new trends in Autonomous Vehicles (AVs). ML algorithms play a crucial role in interpreting sensor data, predicting potential hazards, and optimizing navigation strategies. However, achieving full autonomy in cluttered and complex situations, such as intricate intersections, diverse sceneries, varied trajectories, and complex missions, is still challenging, and the cost of data labeling remains a significant bottleneck. The adaptability and robustness of humans in complex scenarios motivate the inclusion of humans in ML process, leveraging their creativity, ethical power, and emotional intelligence to improve ML effectiveness. The scientific community knows this approach as Human-In-The-Loop Machine Learning (HITL-ML). Towards safe and ethical autonomy, we present a review of HITL-ML for AVs, focusing on Curriculum Learning (CL), Human-In-The-Loop Reinforcement Learning (HITL-RL), Active Learning (AL), and ethical principles. In CL, human experts systematically train ML models by starting with simple tasks and gradually progressing to more difficult ones. HITL-RL significantly enhances the RL process by incorporating human input through techniques like reward shaping, action injection, and interactive learning. AL streamlines the annotation process by targeting specific instances that need to be labeled with human oversight, reducing the overall time and cost associated with training. Ethical principles must be embedded in AVs to align their behavior with societal values and norms. In addition, we provide insights and specify future research directions.

On the Use of Immersive Digital Technologies for Designing and Operating UAVs

Unmanned Aerial Vehicles (UAVs) provide agile and safe solutions to communication relay networks, offering improved throughput. However, their modeling and control present challenges, and real-world deployment is hindered by the gap between simulation and reality. Moreover, enhancing situational awareness is critical. Several works in the literature proposed integrating UAV operation with immersive digital technologies, such as Digital Twin (DT) and Extended Reality (XR), to address these challenges. This paper provides a comprehensive overview of current research and developments involving immersive digital technologies for UAVs, including the latest advancements and emerging trends. We also explore the integration of DT and XR with Artificial Intelligence (AI) algorithms to create more intelligent, adaptive, and responsive UAV systems. Finally, we provide discussions, identify gaps in current research, and suggest future directions for studying the application of immersive technologies in UAVs, fostering further innovation and development in this field. We envision the fusion of DTs with XR will transform how UAVs operate, offering tools that enhance visualization, improve decision-making, and enable effective collaboration.