Modeling and analysis of driver behaviour under shared control through weighted visual and haptic guidance

Wang, Zheng; Zheng, Rencheng; Nacpil, Edric John Cruz; Nakano, Kimihiko · 2022 · Crossref

DOI: 10.1049/itr2.12163

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the need for accurate driver behavior modeling to optimize the design of driver-automation shared control systems, specifically those utilizing haptic guidance steering. While haptic assistance can improve safety and reduce workload, it may also cause conflicts or performance degradation if the driver’s reliance on the system is not properly understood. The authors propose a novel driver model that integrates visual guidance from the road and haptic guidance from the steering wheel through a weighted process. This model hypothesizes that drivers rely on these sensory inputs based on their respective reliabilities, allowing for the quantification of driver interaction and reliance via specific parameters. The study employed a high-fidelity driving simulator experiment involving fourteen male participants performing a lane-following task at a constant speed of 60 km/h. Data were collected during both manual driving and driving with haptic guidance, where the system applied assistive torques to the steering wheel. The proposed model incorporates vehicle dynamics, steering column dynamics, a two-point visual perception model, and a neuromuscular system. Key parameters include $K_d$, representing the steering torque proportional to the target angle, and $K_{hg}$, a reflex gain ranging from 0 to 1 that quantifies the driver’s reliance on haptic guidance. Model parameters were identified using the prediction error method in MATLAB, and the model was validated by comparing simulated trajectories against measured experimental data. The results demonstrated that the proposed model effectively predicted driver behavior. The model achieved an average fitness of 76% for manual driving and 69% for haptic guidance conditions when matching driver input torque. Validation showed that simulated vehicle trajectories closely matched measured trajectories, with an absolute mean error of 0.218 meters across participants. Parameter identification revealed that drivers reduced their steering effort ($K_d$) when relying more heavily on haptic guidance (lower $K_{hg}$). Numerical simulations further evaluated the model under conditions of declined visual attention and system failure. The analysis indicated that higher reliance on haptic guidance improved lane-keeping accuracy for inattentive drivers but resulted in a sudden performance drop if the system failed, suggesting a trade-off between assistance benefits and failure risks. The significance of this work lies in providing a parsimonious driver model capable of predicting human-machine interaction in shared control scenarios. By quantifying driver reliance through specific gains, the model offers a tool for designing and evaluating haptic guidance systems that balance safety improvements with potential risks. The findings suggest that optimal system design must account for individual differences in driver attentiveness and reliance, paving the way for adaptive shared control strategies that dynamically adjust assistance based on real-time driver states.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• automation
• haptic feedback
• steering pattern
• control interfaces
• situational awareness
• manual

Information type

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• Applied Guidance: design guidelines
• Theoretical Contribution: computational model, theory or model