Student Research Track: Towards Low-Cost Driver Supervision: Evaluating a Smartphone Interface for Driver Inattention Monitoring in Simulated Driving

Abstract

Driver assistance and partially automated driving can reduce active control demands on drivers, but they may become distracted or fatigued and must remain ready to supervise or re-engage. Although driver-monitoring systems have been studied extensively, direct camera-based systems are often vehicle-integrated and not uniformly available. We present a low-cost, supplementary Android smartphone interface that uses the smartphone’s front-facing camera as a supplementary driver-monitoring and alerting interface. The prototype combines on-device face analysis and TensorFlow Lite object detection to identify six states: attentive baseline, yawning, prolonged eye closure, phone-like interaction, reading, and face-not-detected. The system was evaluated in a controlled pilot study with 30 participants across 180 trials, measuring classification performance, alert latency, and post-alert recovery. The system detected multiple target behaviours, with strongest performance for prolonged eye closure and face-not-detected, while phone-like interaction and reading remained more challenging. This work contributes early HCI evidence for using smartphones as interactive driver-supervision interfaces, highlighting trade-offs between low-cost sensing, alert timing, user trust, intrusiveness, and attention recovery.