Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Abstract

Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.

Fatigue State Detection for Tired Persons in Presence of Driving Periods

“…Jeon et. al [24] proposed a system that combined both vehicle-based and behavioral-based methods. The method is based on collecting signals from the steering wheel and pedal pressure, then processing these signals with CNN.…”

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

“…Jeon et al proposed a new method for detecting drowsy driving using vehicle sensor data obtained from the steering wheel and pedal pressure [ 50 ]. Based on preliminary experiments, drowsy driving was classified into long-time and short-time drowsy driving types.…”

Demand-Driven Data Acquisition for Large Scale Fleets

“…4 The external system continuously provides the received sensor data to other systems via streaming. 5 Data transmission can be made dependent on a condition evaluated on each vehicle locally. 6 It is possible to collect data from a specific car within a fleet without collecting data from the other cars.…”

“…Besides the commercial demand identified in the insurance market, academia also has many active fields of research that depend on vehicle data. Among these fields are driver behavior identification [3], inference of lane change intentions [4], or drowsiness detection [5]. In addition, access to vehicle data is essential for municipalities to facilitate the transition to Smart Cities [6].…”