An online reinforcement learning-based conflict resolution approach for human-vehicle cooperative systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel conflict resolution approach for cooperative driving systems. It enhances safety and human experience by minimizing trajectory errors and driver-automation conflicts using reinforcement learning.
Area Of Science
- Robotics
- Artificial Intelligence
- Control Systems
Background
- Conflicts between drivers and automation in cooperative driving systems pose significant challenges.
- These conflicts can negatively impact human experience, trust, and overall driving safety.
Purpose Of The Study
- To propose a novel conflict resolution approach for human-vehicle cooperative systems.
- To minimize trajectory tracking errors and mitigate conflicts between human drivers and automation.
Main Methods
- Introduced a vehicle-driver-conflict model incorporating a driver-in-the-loop perspective.
- Developed an online reinforcement learning algorithm to compute the automated steering angle.
- Utilized Lyapunov's theory to ensure model stability and control convergence.
Main Results
- The proposed method effectively minimizes errors in trajectory tracking.
- The reinforcement learning algorithm successfully calculates automated steering angles, even in conflict scenarios.
- Lyapunov's theory guarantees uniform ultimate boundedness of system states and convergence of control signals.
Conclusions
- The developed approach enhances cooperative driving systems by resolving driver-automation conflicts.
- The method improves trajectory tracking and ensures stability through advanced control techniques.
- Numerical simulations validate the effectiveness and superiority of the proposed method compared to existing approaches.
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