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An Adaptive Transition Framework for Game-Theoretic Based Takeover

Dikshant Shehmar, Matthew E. Taylor, Ehsan Hashemi

TL;DR

This work tackles takeover control in automated driving by addressing OOTL-induced delays with an adaptive, game-theoretic shared-control framework. It models the driver and ADAS as a cooperative differential game, where control authority is dynamically redistributed through time-varying weighting matrices and an adaptive transition function that responds to real-time trajectory errors. Driver-specific preferences are incorporated by estimating personalized weighting matrices from data, enabling smoother, safer takeovers with reduced steering effort and improved stability. The approach demonstrates, in ISO lane-change scenarios, that adaptive transitions outperform fixed-time and static-sharing strategies, offering a practical path toward more natural and robust human-automation collaboration.

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.

An Adaptive Transition Framework for Game-Theoretic Based Takeover

TL;DR

This work tackles takeover control in automated driving by addressing OOTL-induced delays with an adaptive, game-theoretic shared-control framework. It models the driver and ADAS as a cooperative differential game, where control authority is dynamically redistributed through time-varying weighting matrices and an adaptive transition function that responds to real-time trajectory errors. Driver-specific preferences are incorporated by estimating personalized weighting matrices from data, enabling smoother, safer takeovers with reduced steering effort and improved stability. The approach demonstrates, in ISO lane-change scenarios, that adaptive transitions outperform fixed-time and static-sharing strategies, offering a practical path toward more natural and robust human-automation collaboration.

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.

Paper Structure

This paper contains 16 sections, 19 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Model Description
  3. Shared Control Steering Framework
  4. Vehicle Model
  5. Control Framework
  6. General Formulation
  7. Cooperative Differential Game Setup for Takeover
  8. Sharing Controlled Strategy
  9. Control Authority Transition Function
  10. Quantitative Evaluation of Transition Functions
  11. Human Driver Tracking Preferences
  12. Experimental results and discussion
  13. Comparison of Transition Strategies
  14. Steering Effort Analysis
  15. Conclusions
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Game theoretic realization for shared control
  • Figure 2: Single Track Vehicle Model
  • Figure 3: Simulator setup
  • Figure 4: Cumulative error with different transitions
  • Figure 5: Control input contribution during lane change
  • ...and 1 more figures