Citation

Vecchiato G, Ahlstrom C, Chuang LL. Front. Neuroergonom. 2022; 3: e897659.

Copyright

(Copyright © 2022, Frontiers Media)

DOI

10.3389/fnrgo.2022.897659

PMID

unavailable

Abstract

Human behavior is often cited as the primary contributing factor to road accidents--over 90% of all crashes are attributed to "human error" (Singh, 2015). This implicitly suggests that accidents could be avoided if only drivers behaved better and has, thus, fuelled enthusiasm for (semi-)automated vehicles, which do not suffer from human frailty and are more likely to follow the rules. Nonetheless, this perspective is flawed. First, most road users strive to avoid road accidents. Second, fatalities persist even with (semi-)automated vehicles and it remains unclear if increased adoption of more automation will change the situation at all (Mueller et al., 2021). Modern perspectives suggest that "human error" is a product of not only individual behavior but the system that we operate within (Read et al., 2021). An individual cannot be understood without taking into account their relationship with the working environment. Safe vehicles are those that enable drivers to act with a minimal margin for unintended error while ensuring that road traffic systems cater to user autonomy. Even if automation can mitigate driving-related risks, it will simply present new challenges that can only be anticipated by first understanding the cognitive mechanisms associated with operating in road traffic systems. To this aim, it is vital that we possess better tools to understand, measure and monitor human behavior and the corresponding cerebral activity across diverse road scenarios, including those that do not generate overt behavior.

This Research Topic invited manuscripts that covered modeling, behavioral and neurophysiological measures investigating conventional and (semi-)automated driving with the goal to develop a safe human-centric road traffic system. The submitted contributions responded to this challenge in various ways, including state-of-the-art physiological measures to assess the driver's mental state, theoretical and empirical methodological approaches to advance the present knowledge, and challenges in vehicle automation...

Language: en