An Approach to Transfer of Control Between Automated Vehicle and Driver

Dubovsky, V.; Savchenko, V. · 2026 · Crossref

DOI: 10.35596/1729-7648-2026-24-3-85-91

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Summary

This paper addresses the safety risks associated with the transfer of control from an automated driving system (ADS) to a human driver in Level 3 automated vehicles. While Level 3 automation allows drivers to disengage from the driving task, they must be prepared to assume manual control if the vehicle exits its operational design domain or encounters a critical failure. The primary challenge is that drivers often suffer from complacency, loss of situational awareness, and degraded skills during extended periods of automation, leading to delayed or inadequate responses during take-over requests (TOR). Although existing methods employ multimodal interfaces to monitor driver state and prompt manual control, the problem remains unresolved due to its interdisciplinary complexity. The study aims to propose a new approach that mitigates these safety risks by incorporating the driver’s individual psychophysiological characteristics (IPC) into the decision-making process for control transfer. The proposed approach involves collecting data on a driver’s stable IPCs—such as motor reaction time, ability to act urgently, concentration, distribution of attention, and risk propensity—during periods of manual driving. These metrics are derived from monitoring vehicle, driver, and environmental states, potentially utilizing collision avoidance systems to detect deviations, unsafe following distances, or speed limit violations under favorable weather conditions. The paper outlines two specific implementation diagrams: one for immediate, binary transfer of control where the system evaluates the driver’s current IPCs against the task demands, and another that integrates shared authority modes (where control is gradually transferred) with IPC-based decision-making. This method contrasts with current practices that primarily focus on characterizing the driver’s transient state (e.g., drowsiness or distraction) without predicting their capacity to mobilize internal resources for critical decision-making. The significance of this approach lies in its potential to enhance the flexibility, validity, and reliability of control transfer decisions. By accounting for intrinsic individual variability in psychophysiological traits, the system can better assess whether a driver is capable of safely assuming control in critical situations where shared control might be too risky or time-constrained. The authors argue that using IPCs as predictors of a driver’s ability to mobilize necessary resources addresses a gap in existing research, which largely overlooks the predictive value of stable individual characteristics. This framework offers a more robust solution for ensuring safe transitions between automated and manual driving modes, ultimately contributing to improved road safety in conditional automation systems.

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Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.

Topics

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• automation
• takeover transitions
• automation surprise
• mode awareness
• situational awareness
• manual

Information type

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• Methodological Resource: measurement protocol
• Theoretical Contribution: conceptual framework, computational model