Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

TL;DR

This paper evaluates embedded platforms for real-time driver drowsiness detection, identifying the Beelink Mini PC as the most practical choice, and introduces a threshold optimization method to improve detection accuracy.

Contribution

It demonstrates the suitability of the Beelink Mini PC for embedded drowsiness detection and proposes a threshold optimization algorithm to enhance detection performance in vehicles.

Findings

Beelink Mini PC has an average processing time of 22.73 ms for inference.

Jetson Nano's processing time is 94.27 ms, less suitable for real-time detection.

Threshold optimization improves sensitivity and specificity trade-offs.

Abstract

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.