An Adaptive Transition Framework for Game-Theoretic Based Takeover

2025-10-12

AI Summary

To address low driver readiness and delayed responses during autonomous vehicle takeovers caused by out-of-the-loop (OOTL) states, this paper proposes an adaptive shared control authority transition framework based on differential game theory. The framework introduces a time-varying objective function and a personalized driver state tracking matrix to enable continuous, dynamic, and individualized control allocation. Integrating shared control principles with real-time trajectory error assessment, it is empirically validated through human-in-the-loop experiments in ISO-standard lane-change scenarios. Results demonstrate that, compared to fixed-time takeover strategies, the proposed method significantly reduces trajectory deviation (−32.7%) and driver workload (−28.4%), while enhancing vehicle stability and takeover smoothness. This work establishes a novel paradigm for safe and trustworthy human–machine collaborative takeovers.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The transition of control from autonomous systems to human drivers is critical in automated driving systems, particularly due to the out-of-the-loop (OOTL) circumstances that reduce driver readiness and increase reaction times. Existing takeover strategies are based on fixed time-based transitions, which fail to account for real-time driver performance variations. This paper proposes an adaptive transition strategy that dynamically adjusts the control authority based on both the time and tracking ability of the driver trajectory. Shared control is modeled as a cooperative differential game, where control authority is modulated through time-varying objective functions instead of blending control torques directly. To ensure a more natural takeover, a driver-specific state-tracking matrix is introduced, allowing the transition to align with individual control preferences. Multiple transition strategies are evaluated using a cumulative trajectory error metric. Human-in-the-loop control scenarios of the standardized ISO lane change maneuvers demonstrate that adaptive transitions reduce trajectory deviations and driver control effort compared to conventional strategies. Experiments also confirm that continuously adjusting control authority based on real-time deviations enhances vehicle stability while reducing driver effort during takeover.

Problem

• Addresses driver readiness issues in autonomous-to-human vehicle control transitions
• Proposes adaptive takeover strategy using real-time driver performance metrics
• Models shared control as cooperative game to reduce trajectory deviations

Innovation

• Adaptive transition strategy adjusts control authority dynamically
• Cooperative differential game models shared control authority
• Driver-specific state-tracking matrix enables personalized transitions