Expert Drivers’ Prospective Thinking-Aloud to Enhance Automated Driving Technologies – Investigating Uncertainty and Anticipation in Traffic

Grahn, Hilkka; Kujala, Tuomo; Silvennoinen, Johanna; Leppänen, Aino; Saariluoma, Pertti

doi:10.1016/j.aap.2020.105717

Cited by 13 publications

(6 citation statements)

References 64 publications

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“…In [26], the authors explored the application of TAP methodology to prove its validity for deducing driving rules. In [27], the aim was to understand how expert drivers (i.e., driving instructors) react and anticipate to driving startles, in order to provide similar anticipatory capacities to automated driving technologies. Efficient driving has also been studied in [28], where the think aloud protocol method was used to identify the mental models of eco-driving by conducting behavioral measures.…”

Section: Approach and Methodologymentioning

confidence: 99%

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

et al. 2023

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Learning from human driver's strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods for designing ADAS systems, as well as for design selfdriving rules for automated vehicles. This paper proposes a human-like decision-making algorithm built up from human drivers experiential naturalistic driving. The approach of this work consists of exploring two main techniques. Firstly, the use of "think aloud protocol" to build a dataset based on naturalistic driving, capturing driver's intentions. Afterwards, the technique of decision tree is used to generate an algorithm to categorize driving patterns as a function of circumstantial driving parameters. The study is focused on simple roundabouts in presence of other vehicles. The decision tree is translated into algorithmic rules, where the tree pathways are represented as 'if-then' rules. Finally, the accuracy of the driver behavior model has been assessed, yielding promising results.

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Section: Approach and Methodologymentioning

confidence: 99%

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

et al. 2023

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“…Microsoft's robot editor (Akhavan et al, 2018;Bawack et al, 2021;Botega & da Silva, 2020;Casazza & Gioppo, 2020;Grahn et al 2020;Holford 2019;Köbis & Mossink, 2021;Kronblad 2020;S. K. Parker et al, 2021;Townsend and Hunt 2019;Wu et al 2020;Xu and Wang 2019) AI triggers 'fear' in workers, and this 'fear' stimulates workers' IWB (Abubakar et al, 2019;Braganza et al 2020;Cetindamar Kozanoglu & Abedin, 2020;Cropley 2020;Ding, 2021;Druckman et al 2021;Gruchmann et al, 2020;Holford 2019;Kronblad 2020;Loureiro et al 2020;Palumbo 2021;Wu et al 2020;Xu and Wang 2019) Workers are reskilled and upskilled to compensate for AI limitations (Birdi, 2020;Butschan et al, 2019;Casazza & Gioppo, 2020;Cetindamar Kozanoglu & Abedin, 2020;Chaubey & Sahoo, 2018;Cropley 2020;Dutse, 2015;Grahn et al 2020 used a photo of the wrong mixed-race member of a band to illustrate a news article about racism (Waterson 2020b). An AI robot camera, which was meant to monitor the football during a game, instead tracked the assistant referee's head, resulting in sudden camera movements towards the referee and repeated switching between the referee's head and the actual football (HT Tech, 2020).…”

Section: Robots Make Mistakes and Such Mistakes Stimulate Workers' Iwbmentioning

confidence: 99%

“…Although this can come as a surprise, since robots are supposed to make fewer errors, workers' IWB may benefit from robots' mistakes (Choi et al 2019;Yam et al 2020). Delivery drivers, for example, can drive in real traffic while predicting events for a robotic autopilot (Grahn et al 2020). Although robotic autopilot can manage predicted events in this scenario, workers can deal with uncertainty and related adaptive and social behaviours in specific, highly congested traffic conditions and environments (Grahn et al 2020).…”

Section: Robots Make Mistakes and Such Mistakes Stimulate Workers' Iwbmentioning

confidence: 99%

Can artificial intelligence’s limitations drive innovative work behaviour?

Zirar

2023

Rev Manag Sci

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Artificial intelligence (AI) is deemed to increase workers' productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI's ability to create value through innovation, less is known about how AI's limitations drive innovative work behaviour (IWB). With AI's limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers' innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers' IWB, (ii) AI triggers 'fear' in workers, and this 'fear' stimulates workers' IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a generalpurpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.

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“…Even though participants could state a reason for their decision, they were not instructed to mention all factors that influenced them in the respective scenarios. Methods like the thinking aloud technique (Grahn et al, 2020) could help to clarify which aspects of a situation led to the participants' decision and make the decision process more visible. Furthermore, future work should examine in more detail what information an automated vehicle should provide to drivers concerning context factors in urban environments with crossing pedestrians.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Lanzer¹,

Stoll²,

Colley

et al. 2021

Front. Hum. Dyn.

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Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.

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Expert Drivers’ Prospective Thinking-Aloud to Enhance Automated Driving Technologies – Investigating Uncertainty and Anticipation in Traffic

Cited by 13 publications

References 64 publications

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

Can artificial intelligence’s limitations drive innovative work behaviour?

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

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