A Brain Wave-Verified Driver Alert System for Vehicle Collision Avoidance

Abstract

Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking. Thirty (30) subjects completed a driving simulator experiment under different near-collision scenarios. The Steady-State Visually Evoked Potentials (SSVEP) of the drivers’ brain signals and their collision mitigation (control performance) data were analyzed to evaluate the LED warning system’s effectiveness. The results show that (1) The proposed modified canonical correlation analysis evaluation (CCA-EVA) algorithm can detect SSVEP responses with 4.68% higher accuracy than the Adaptive Kalman filter; (2) The proposed driver monitoring and alert system produce on average a 52% improvement in time to collision (TTC), 54% improvement in reaction distance (RD), and an overall 26% reduction in collision rate as compared to similar tests without the LED warning.

EEG-fest: few-shot based attention network for driver's drowsiness estimation with EEG signals

“…The counter-steering event recorded when the subject responds to the drift is used to determine the reaction time [23]. The drowsiness index can be calculated using equation (16) with the react time t, and a value close to 1 indicates the subject is drowsy [31]. EEG samples were recorded from 32 channels for each subject, and a moving average filter with a window length of 10 was applied to smooth the drowsiness index.…”

“…One of the most widely used direct measurement tools for analyzing driver drowsiness is Electroencephalography (EEG), a physiological sensor that measures brain signals in humans [15,16]. Advanced pattern recognition algorithms are necessary to accurately decipher EEG signals, as these signals are frequently non-stationary and prone to contamination from noise sources such as eye movements [17].…”