Automation complacency on the road

Chu, Yueying; Liu, Peng

doi:10.1080/00140139.2023.2210793

Cited by 6 publications

(1 citation statement)

References 101 publications

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“…In these non‐fully AVs, human drivers transition from active controllers to passive supervisors, necessitating vigilance to monitor machine drivers and the road, intervening when necessary. This handover of control can lead to short‐term human risks such as distractions, inattention, and boredom, as well as long‐term human risks such as skill atrophy, making humans unreliable backups on time‐critical and complex roads (Chu & Liu, in press; Hancock, 2019). Considering their negative influences on human drivers, machine drivers may have a negative net impact.…”

Section: Discussionmentioning

confidence: 99%

Machines meet humans on the social road: Risk implications

Liu

2023

Risk Analysis

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Human drivers and machine drivers (i.e., automated vehicles or AVs) will share roads and interact with each other, creating mixed traffic. In this perspective, we develop two mental models about them and their social interactions, aiming to understand the risk implications of AVs and mixed traffic. Based on Mental Model I (i.e., machine drivers are superior drivers without human weaknesses), many simulation‐based safety assessments, which often overlook or oversimplify human‐AV social interactions, have predicted significant safety benefits when machine drivers interact with or replace human drivers. In contrast, Mental Model II considers human and machine drivers as heterogeneous and incompatible, suggesting that their interactions may lead to unexpected and occasionally negative outcomes, particularly in imminent mixed traffic. This perspective gains support from recent comparative empirical studies that employ various methods such as survey experiments, driving simulators, test‐tracks, on‐road observations, and AV accident analysis. These studies provide initial evidence of emerging traffic risks arising from human‐AV social interactions, including human drivers’ aggression and road rage toward AVs, human drivers exploiting AVs, AVs exerting negative peer influences on human drivers, and their incompatibility increasing human drivers’ challenges in joining mixed traffic and thus risky behaviors. We propose specific suggestions to mitigate problematic human‐AV social interactions and the associated emerging risks.

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Section: Discussionmentioning

confidence: 99%

Machines meet humans on the social road: Risk implications

Liu

2023

Risk Analysis

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Research on the influence and evaluation of manual operation performance of multifunctional operation panel considering interaction effect

Zhang,

Ren,

Sun

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Manual operations remain crucial in critical human‐machine interactions due to limitations in automatic control algorithms and technologies. While ergonomic analysis and evaluation techniques for interactive interfaces are advancing, recent approaches emphasize integrating graphical interface experiences with intuitive controls. Conventional methods often lack precision or overlook the interaction effect of different influencing factors, leading to inadequate assessment of essential manual operations for intricate interfaces, such as multifunctional operation panels. To address these challenges, this study aimed to investigate the interaction effects of various factors on the manual operation performance of operators when using a multifunctional operation panel and aims to develop a more comprehensive and broadly applicable evaluation model for such panels. An experiment was designed to consider the type, size, layout of controls, and operational task type as the main factors affecting manual operation performance. Multivariate analysis of variance was used to identify significant interaction effects among the operation factors. The findings underscored the importance of these interactions in evaluating manual operation performance. Multivariate linear regression further examined the influence of these factors, enhancing the evaluation methodology. The study emphasizes the critical role of understanding interaction effects in assessing the manual operation performance of multifunctional operation panels, particularly in improving the design of the panel or operation tasks.

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