Left Gaze Bias Between LHT and RHT: A Recommendation Strategy to Mitigate Human Errors in Left- and Right-Hand Driving

Driver errors, such as distraction, perceptual blindness, and incorrect control manipulation, can either cause road accidents or reduce driving performance in daily driving tasks. Several works in literature have illustrated perceptual blindness and distraction are associated with insufficient attention to those activities vital for safe driving. Also, inappropriate driving-related eye movements may subsequently result in manipulation errors, such as inappropriate control of the throttle or steering wheel. Although many studies have examined drivers' visual performance, there have been few attempts to compare left-hand traffic (LHT) and right-hand traffic (RHT) conditions. Even driving in the same driving scene, different eye movement patterns may also be induced due to drivers' left gaze bias. Motivated by this human factor, this research investigates potential human errors in driving from the perspective of eye movements (i.e., saccades and fixations) and corresponding control manipulations. Firstly, a driving simulator is used to investigate the discrepancy of eye movements in identical driving scenes between LHT and RHT. Secondly, visual regions and feature sensitivities are spatially correlated by comparing the gaze distribution from the central to peripheral visual fields for both LHT and RHT. Besides, considering the violation of traffic rules are likely the cause of potentially fatal driving eye movement and control mistakes, a recommendation strategy is proposed to alleviate visual errors by utilizing a neural network inspired by the driver gaze map. The experiments in both LHT and RHT driving tasks demonstrate improvements in the alleviation of human errors by importing the proposed eye movement recommendation strategy.

Automated summary

Driver errors, such as distraction and perceptual blindness, can lead to accidents or decreased driving performance. This study investigates potential human errors in driving by comparing eye movement patterns in left-hand traffic and right-hand traffic conditions.