The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve

Abstract

It remains uncertain regarding the safety of driving in autonomous vehicles that, after a long, passive control and inattention to the driving situation, how the drivers will be effectively informed to take-over the control in emergency. In particular, the active role of vehicle force feedback on the driver's risk perception on curves has not been fully explored. To investigate it, the current paper examined the driver's cognitive and visual responses to the whole-body haptic feedback during curve negotiations. The effects of force feedback on drivers' responses on curves were investigated in a high-fidelity driving simulator while measuring EEG and visual gaze over ten participants. The preliminary analyses of the first two participants revealed that pupil diameter and fixation time on the curves were significantly longer when the driver received whole-body feedback, compared to none. The findings suggest that whole-body feedback can be used as an effective "advance notification" of hazards.

A Review of Driving Simulation Technology and Applications

“…In this work, the impact this system has on driver's fatigue during monotonous driving is investigated by monitoring the standard deviation of lateral position. The benefit of haptic feedback is also evaluated in [116], where the authors found that whole-body feedback during curves can be effective in avoiding hazardous situations when take-over is needed. Furthermore, in [117], a real-time adaptable haptic feedback is proposed based on the level of distraction of the driver.…”

A Comprehensive Review of Driver’s Attention and the Evaluation Methods

“…Vision has been asserted as the largest single resource available to the driver and the primary processing input in driving [14]. Behavioral measures like eye movement patterns and head positions can easily be collected by eye trackers, or by application-specific computer vision techniques [13,30,31,32] Various measures from eye movement matrices like eye blinks, fixation, gaze angle, pupil dilation, and glance duration have been used to find distraction [14,29,33,34]. It was found that distracted driving is in association with the high frequency of off-road glances, longer total eye-off-road time [17], high frequency and duration increment of eye blink, and dilated pupil [35,36].…”

DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

“…Nevertheless, due to technology limitations and legal restrictions, automated vehicles (AVs) [9] may still need to handover the control back to drivers occasionally (e.g., under challenging driving conditions beyond the automated systems' capabilities) [37]. In such cases, AVs would initiate takeover requests (TORs) and alert drivers via auditory, visual, or vibrotactile modalities [42,45,59] so that the drivers can resume manual driving in a timely manner. However, there are challenges in making drivers safely take over control.…”