Abstracts Track 2021

Area 1 - Intelligent Vehicle Technologies

Nr: 2

Electroencephalography Shows Effects of Workload for Oddball Auditory Signals: Implications for Semi-autonomous Vehicle Alerting Systems


Melanie Turabian, Kathleen Van Benthem and Chris Herdman

Abstract: Features and capabilities of semi-autonomous vehicles are advancing at an impressive rate, however, humans are expected to remain in-the-loop for the next few decades, at least. Human-machine-interaction (HMI) during driving is still required, such as during "handovers" whereby the driver must take control of the vehicle (McCall et al., 2019). Furthermore, humans must remain vigilant during the driving task as they attend and respond to visual and auditory alerts that indicate when additional collaboration is required. Thus, understanding how multi-modal information is processed during primarily visual tasks is important, particularly as this relates to auditory alerts in semi-autonomous vehicles. This research evaluated neural processing of auditory signals while participants were engaged in a visuospatial task of varying difficulty levels. The goal of this research was to evaluate the efficacy of auditory alerting signals that may exist in semi-autonomous vehicles. This study included electroencephalography (EEG) and behavioural data from 10 participants (18-80 years old). Outcome measures consist of response time, accuracy for a match-to-sample visuospatial task, and event-related potentials (ERPs) derived from a 128-channel dense array EEG system. We investigated how participants performed on low- and high-workload match-to-sample visuospatial tasks, as well as their neurological responses to the second redundant auditory signal from a passive paired-stimulus oddball EEG paradigm. Participants had lower accuracy and slower response times in the high-workload condition of the visual task. Analyses of the EEG data displayed a main effect of auditory signal type whereby at the somatosensory association cortex, following the presentation of a standard tone, the deviant tone resulted in an attenuated P200 component. Thus, suggesting that even deviant stimuli at this brain region are being classified as redundant. These findings point to the importance of designing auditory alerting systems such that redundancies are taken into consideration, particularly when used to inform drivers of important events, such as handovers. Auditory processing of signals during visual tasks must be considered given that safety features related to semi-autonomous vehicles, such as alerting systems, are still being developed. References: McCall, R., McGee, F., Mirnig, A., Meschtscherjakov, A., Louveton, N., Engel, T., & Tscheligi, M. (2019). A taxonomy of autonomous vehicle handover situations. Transportation research part A: policy and practice, 124, 507-522.

Nr: 3

People’s Preferences for Different Uses of Autonomous Vehicles in China


Yulu Lin

Abstract: This paper draws from people’s choices of different services in autonomous vehicles to generate new insights about their acceptance of autonomous vehicles to meet their mobility needs. This article highlights the relationship between people’s mindfulness and their choice of the following applications of autonomous driving technology: self-driving-uber, car-sharing (Car2go), subway/rail, rental car, paratransit, self-owned autonomous car, self-driving car subscription, and self-driving bus. A quantitative analysis shows that in Chinese metropolitan areas, mindful people’s trust, health situations, travel costs, knowledge of autonomous vehicles, and their willingness to change their travel mode are significantly related to their choosing autonomous paratransit as their preferred mode. Travel costs have a significant effect on participants’ preference for autonomous vehicles. These findings indicate that using shared autonomous vehicles in paratransit services can improve high travel costs’ people’s trust in autonomous vehicles. This indicates that price has significant impact on people's preference of different uses of Autonomous Vehicles in Chinese metropolitan area.