Document Type
Report
Abstract
There are six levels of vehicle automation, from Level 0 – no automation to Level 5, fully autonomous [1]. According to several projections, the majority of vehicles on the road will be at intermediate levels for the next several years, meaning that vehicle-to-human takeover will be required in cases where the systems can no longer function due to design limitations, such as under poor weather conditions or in a construction zone [2], [3]. As shown in Fig. 1, the takeover process consists of signal response and post-takeover phases, which involves multiple steps, including perceiving the takeover requests (TOR), moving hands and foot to prepare for manually controlling the vehicle, asesssing information in the driving environment, strategizing maneuvering plans, and executing actions. The process generally lasts a few seconds and could be very challenging if drivers are engaged in non-driving-related tasks [4] that utilize visual and auditory resources that results in them being out-of-the-loop. In this case, Multiple Resource Theory (MRT) [5] posits that drivers’ ability to process critical warning information, i.e., a TOR presented via the visual/auditory channels, may be negatively impacted. Therefore, information in the driving environment, that should be acknowledged by drivers, could be conveyed in a more available sensory channel, i.e., the tactile modality. However, given that information presented in the tactile channel can appear in many (complex) formats with different associated meanings [6], [7], it is critical to assess drivers’ ability to comprehend meaningful tactile patterns and determine its effectiveness on drivers’ takeover performance. The goal of this project was to investigate the effects of meaningful tactile patterns on automated vehicle takeover performance. In particular, vibrotactile devices were embedded into the seat pan and seatback of a medium-fidelity simulated vehicle. The patterns represented two formats: informative (i.e., communicating the status of surrounding vehicles or obstacles) and instructional (i.e., commanding particular maneuvering actions such as driving into left/right lanes). Participants were required to complete a series of takeover events, where meaningful tactile patterns served as takeover requests.
Publication Date
7-2021
Recommended Citation
Huang, Gaojian, "Using Advanced Driving Simulation and Vibrotactile Cues to Train Drivers to Interact with Next-Generation Autonomous Vehicles" (2021). Link Foundation Modeling, Simulation and Training Fellowship Reports. 31.
https://repository.fit.edu/link_modeling/31
Standard cover form for report
Comments
Link Foundation Fellowship for the years 2020-2021.