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Motion Cues and Field of View in Virtual Truck-Driver-Training Under Crosswinds: Effects on Driving Control, Simulator Sickness, and User Experience

Sep 2026 · Transportation Research Record · 0 citations · 33 references

Abstract

Virtual reality (VR)-based training provides a safe and cost-effective solution for truck-driver-training tasks. However, the effectiveness of VR training in replicating real-world driving conditions remains underexplored. This study examines how vestibular motion cues and field of view (FOV) affect driving control, VR sickness, and user experience in truck-driving simulations under crosswind disturbances. Fifty-four participants (35 undergraduates and 19 truck drivers) performed simulated highway driving tasks under Motion (vestibular feedback) and No Motion conditions, using either a head-mounted display (HMD) with a larger vertical FOV or a three-screen setup. Steering control was assessed via wind-steering correlation (the alignment between steering adjustments and wind disturbances), entropy, and reversal rate (corrective steering adjustments), while VR sickness and user experience were evaluated using standardized questionnaires. The absence of vestibular motion cues in the HMD condition led to poorer steering control, reflected in lower wind-steering correlation, higher entropy, and increased corrective adjustments. It also received the lowest ratings for perspicuity and efficiency, particularly among experienced drivers. Simulator sickness symptoms (nausea, oculomotor strain, disorientation) increased in the absence of motion cues in the HMD condition. Overall, motion conditions improved steering performance and were rated higher in efficiency, perspicuity, and novelty. These findings underscore the importance of integrating vestibular motion cues and realistic disturbances in VR truck training to support skill transferability. Optimizing FOV and motion feedback can enhance training fidelity, reduce simulator sickness, and improve driver adaptation. This research informs VR simulator design, improving safety, efficiency, and cost-effectiveness in driver education programs.

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