Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Jayaraman, Suresh Kumaar; Creech, Chandler; Tilbury, Dawn M.; Yang, Xi; Pradhan, Archana; Tsui, Katherine M.; Robert, Lionel

doi:10.3389/frobt.2019.00117

Cited by 75 publications

(54 citation statements)

References 65 publications

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“…For many, aging can correspond with greater difficulty in driving, and according to the US Census Bureau, approximately 70 million individuals in the United States will be over the age of 64 by 2030 ( Figure 1) [4]. This explains why AVs are suggested as one potential solution, but a lack of trust hinders that adoption across all age groups [5,6]. The Society of Automotive Engineers (SAE) classifies driving automation into six levels ranging from 0 to 5, as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Drivers’ Age and Automated Vehicle Explanations

2021

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Automated vehicles (AVs) have the potential to benefit our society. However, lack of trust is a major barrier to the adoption of AVs. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about AVs’ decision-making and action. However, explanations might increase drivers’ cognitive effort and anxiety. Because of differences in cognitive ability across age groups, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust, effort, and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-aged, and older). Participants were presented with: (1) no explanation, (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results suggest that the explanations provided before AVs take action produced the highest trust and lowest effort for all drivers regardless of age group. The request-for-permission condition led to the highest trust and lowest effort only for older drivers. Younger drivers had the lowest anxiety and effort under the AV-explanation-after-action condition; however, this condition produced the highest level of anxiety and effort in middle-aged and older drivers, respectively. These results have important implications in designing AV explanations and promoting trust.

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Section: Introductionmentioning

confidence: 99%

Drivers’ Age and Automated Vehicle Explanations

2021

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“…For model training and testing, we isolated scenes that had at least one AV interacting with the pedestrian. More details about the data collection methodology and the user study can be found in [17], [65].…”

Section: A Datasetsmentioning

confidence: 99%

Multimodal Hybrid Pedestrian: A Hybrid Automaton Model of Urban Pedestrian Behavior for Automated Driving Applications

et al. 2021

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For automated vehicles (AVs) to navigate safely, they must be able to anticipate and predict the behavior of pedestrians. This is particularly critical in urban driving environments where risks of collisions are high. However, a major challenge is that pedestrian behavior is inherently multimodal in nature, i.e., pedestrians can plausibly take multiple paths. This is because, in large part, pedestrian behaviors are driven by unique intentions and decisions made by each pedestrian walking along a particular sidewalk or crosswalk. As described in this paper, we developed a hybrid automaton model of multimodal pedestrian behavior called Multimodal Hybrid Pedestrian (MHP). We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton framework. The resulting MHP model is more likely to predict the ground truth trajectory compared to two baseline models-a baseline hybrid automaton model and a constant velocity model. The MHP model is applicable to a wide variety of urban scenarios-midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion planning. INDEX TERMS pedestrian trajectory prediction; pedestrian behavior recognition; human-centered robotics; advanced driver assistance systems (ADAS); autonomous vehicle navigation.

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“…Reig et al [64] studied pedestrian trust in autonomous vehicles via interviews, showing for example that participants who were favourable to AVs were more likely to trust them and that the lack of knowledge about AV technology leads to mistrust. Using the definition of trust in [46] introduced above, Jayaraman et al [38] studied pedestrians' trust in autonomous vehicles in a VR experiment followed by a questionnaire using a Likert scale. It is argued that human trust increases with the increase of available information, and found that the AV's driving behaviour and the presence of light can influence the trust of pedestrians.…”

Section: Trust In Human-av Interactionsmentioning

confidence: 99%

Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

Camara

Fox

2020

Int J of Soc Robotics

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Understanding pedestrian proxemic utility and trust will help autonomous vehicles to plan and control interactions with pedestrians more safely and efficiently. When pedestrians cross the road in front of human-driven vehicles, the two agents use knowledge of each other’s preferences to negotiate and to determine who will yield to the other. Autonomous vehicles will require similar understandings, but previous work has shown a need for them to be provided in the form of continuous proxemic utility functions, which are not available from previous proxemics studies based on Hall’s discrete zones. To fill this gap, a new Bayesian method to infer continuous pedestrian proxemic utility functions is proposed, and related to a new definition of ‘physical trust requirement’ (PTR) for road-crossing scenarios. The method is validated on simulation data then its parameters are inferred empirically from two public datasets. Results show that pedestrian proxemic utility is best described by a hyperbolic function, and that trust by the pedestrian is required in a discrete ‘trust zone’ which emerges naturally from simple physics. The PTR concept is then shown to be capable of generating and explaining the empirically observed zone sizes of Hall’s discrete theory of proxemics.

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Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Cited by 75 publications

References 65 publications

Drivers’ Age and Automated Vehicle Explanations

Drivers’ Age and Automated Vehicle Explanations

Multimodal Hybrid Pedestrian: A Hybrid Automaton Model of Urban Pedestrian Behavior for Automated Driving Applications

Space Invaders: Pedestrian Proxemic Utility Functions and Trust Zones for Autonomous Vehicle Interactions

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