Shared Control for Assistive Driving: Stackelberg Meta-Learning Approach

Belangrijkste concepten

Efficiently collaborate with human drivers using Stackelberg meta-learning for shared control in assistive driving.

Samenvatting

The article introduces a Stackelberg meta-learning algorithm for shared control in assistive driving. It addresses challenges like bounded rationality, asymmetric collaboration, and uncertainties in human behaviors. The algorithm adapts to different driver types and assists them effectively in reaching the target destination while saving driving time.

Statistieken

An effective collaboration plan needs to learn and adapt to uncertainties.
The meta-learning algorithm generates a common behavioral model.
The adapted human behavioral model successfully assists drivers in reaching the target destination.
Saves driving time compared to a driver-only scheme.
Robust to drivers' bounded rationality and errors.

Citaten

"Shared control allows the human driver to collaborate with an assistive driving system while retaining the ability to make decisions and take control if necessary."
"The meta-learning algorithm generates a common behavioral model, which is capable of fast adaptation using a small amount of driving data."
"We use a case study of an obstacle avoidance driving scenario to corroborate that the adapted human behavioral model can successfully assist the human driver in reaching the target destination."

Belangrijkste Inzichten Gedestilleerd Uit

Stackelberg Meta-Learning Based Shared Control for Assistive Driving

by Yuhan Zhao,Q... om arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10736.pdf

Diepere vragen

How can this Stackelberg meta-learning approach be applied to other domains beyond autonomous vehicles

The Stackelberg meta-learning approach developed for shared control in autonomous vehicles can be applied to various other domains beyond just driving. One potential application is in human-robot collaboration, where robots and humans work together on tasks that require coordination and cooperation. For example, in manufacturing settings, robots can assist human workers by learning from their actions and adapting to different working styles or preferences. This approach could also be used in healthcare robotics, where robots collaborate with medical professionals to provide better patient care based on individual needs and preferences.

What are potential drawbacks or limitations of relying on automated learning-based planning for shared control

While automated learning-based planning for shared control offers many benefits, there are some potential drawbacks and limitations to consider. One limitation is the reliance on data quality and quantity for effective learning. If the training data is biased or insufficient, it may lead to suboptimal decision-making by the system. Additionally, automated learning algorithms may struggle with complex real-world scenarios that involve high levels of uncertainty or rapidly changing environments. Another drawback is the interpretability of the learned models - complex machine learning algorithms may produce results that are difficult to understand or explain.

How might advancements in meta-learning impact the future development of autonomous systems

Advancements in meta-learning have the potential to significantly impact the future development of autonomous systems by enabling more adaptive and flexible behavior. Meta-learning allows systems to quickly adapt to new tasks or environments with minimal additional training data, making them more versatile and efficient. In the context of autonomous systems, this means that robots or AI agents can learn from past experiences across a wide range of tasks and apply this knowledge effectively when faced with new challenges. This adaptability could lead to safer and more reliable autonomous systems that can operate effectively in dynamic and unpredictable environments.