In-Vehicle Human Machine Interface: Investigating the Effects of Tactile Displays on Information Presentation in Automated Vehicles

Abstract

Background: Semi-autonomous vehicles still require human drivers to take over when the automated systems can no longer perform the driving task. Objective: The goal of this study was to design and test the effects of six meaningful tactile signal types, representing six driving scenarios (i.e., navigation, speed, surrounding vehicles, over the speed limit, headway reductions, and pedestrian status) respectively, and two pattern durations (lower and higher urgencies), on drivers' perception and performance during automated driving. Methods: Sixteen volunteers participated in an experiment utilizing a medium-fidelity driving simulator presenting vibrotactile signals via 20 tactors embedded in the seat back, pan, and belt. Participants completed four separate driving sessions with 30 tactile signals presented randomly throughout each drive. Reaction times (RT), interpretation accuracy, and subjective ratings were measured. Results: Results illustrated shorter RTs and higher intuitive ratings for higher urgency patterns than lower urgency patterns. Pedestrian status and headway reduction signals were associated with shorter RTs and increased confidence ratings, compared to other tactile signal types. Lastly, among six tactile signals, surrounding vehicle and navigation signal types had the highest interpretation accuracy. Conclusion: These results will be used as preliminary data for future studies that aim to investigate the effects of meaningful tactile displays on automated vehicle takeover performance in complex situations (e.g., urban areas) where actual takeovers are required. The findings of this study will inform the design of next-generation in-vehicle human-machine interfaces.INDEX TERMS Human-machine interface, tactile displays, automated driving, takeover request. I. INTRODUCTION 19 Autonomous vehicles come with great benefits, such as 20 are engaging in non-driving-related tasks (NDRTs) such as 40 reading or texting at the time of takeover, which could lead 41 to higher cognitive workload and longer reaction times to 42 takeover requests and potential threats [7], [8] and can ulti-43 mately result in a driver's possible failure to successfully 44 take over. The criticality of this process could then be further 45 exacerbated when sensory information in the driving environ-46 ment is overwhelming, leading to an overload in the drivers' 47 sensory channels. For example, drivers need to reorient (pay 48 attention to the road) and regain situation awareness during 49 the takeover process [6], [9], [10]. However, a takeover in 50 complex environments such as urban areas that are already 51 filled with a plethora of visual and auditory information 52 to be perceived and processed, e.g., the status/location of 53 surrounding/oncoming obstacles/objects, including but not 54 limited to vehicles, pedestrians, traffic signs/signals, all of 55 which may lead to the overstimulation of a drivers' visual 56 and auditory resources. There lies a need for a reliable 57 human-machine interface (HMI) that utilizes idle sens...

Assisted Partial Take-Over in Conditionally Automated Driving: A User Study

“…Among the earlier studies on TOR modality, Petermeijer et al observed shorter reaction times for auditory or tactile modality compared to visual modality [12]. Researchers agree that the driver's tactile input channel is the least overloaded during driving, and thus many studies focus precisely on tactile UIs [13]- [15]. Vibrations or different tactile patterns alone generally did not improve reaction time itself, but reduced driver effort and increased situational awareness [16], [17].…”