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Personalizing In-Vehicle warnings: A causal machine learning approach to optimizing workload and risk perception.

Zhipeng Peng1, Yihe Huo2, Chenzhu Wang3

  • 1School of Economics and Management, Xi'an Technological University, Xi'an 710021, China.

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|April 16, 2026
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Summary

Personalized in-vehicle Connected Information (CI) delivery, matching warning timing and modality to traffic context and driver state, significantly reduces driver workload and perceived risk for safer driving.

Keywords:
Adaptive Warning SystemsCausal Machine LearningConnected InformationDriver HeterogeneityHuman-Machine InteractionSubjective response

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Area of Science:

  • Human-Computer Interaction
  • Traffic Safety
  • Machine Learning

Background:

  • Current in-vehicle Connected Information (CI) systems often use uniform warning strategies.
  • These strategies fail to account for the complex interplay between warning design, traffic context, and individual driver states.
  • This limitation hinders the overall effectiveness and safety benefits of CI.

Purpose of the Study:

  • To develop a causal machine learning framework to quantify the effects of CI modality and lead time on driver workload (WL) and perceived risk (PR).
  • To investigate how these effects vary across different traffic scenarios and driver states.
  • To provide a quantitative basis for adaptive CI systems.

Main Methods:

  • A 3x3x5 factorial driving-simulator experiment involving 52 participants.
  • Collection of multimodal data: physiological, behavioral, and self-reported.
  • Application of Double Machine Learning with Causal Forests (DML-CF) to analyze causal effects.

Main Results:

  • CI effectiveness is highly context-dependent with significant nonlinear patterns.
  • Optimal warning lead times (5.5–6.5s) and dual-modality cues effectively reduced WL and PR.
  • Delayed or single-modality cues often increased cognitive load and perceived hazards.
  • Conditional Average Treatment Effect (CATE) analysis revealed significant heterogeneity in driver responses.

Conclusions:

  • Adaptive CI systems are crucial for optimizing driver safety and human-machine interaction.
  • Tailoring warning parameters (modality, lead time) to situational uncertainty and individual driver states is essential.
  • Findings support the development of intelligent CI systems that dynamically adjust information delivery.