The future of pedestrian-automated vehicle interactions

Robert, Lionel

doi:10.1145/3313115

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Even this, however, may not be sufficient to comfort individuals concerned about uncertain algorithm performance, such as how vehicles will perform in the absence of sensor data or external communications [29,30]. Studies, therefore, explore the best ways for autonomous vehicles to communicate information with human drivers and pedestrians in real time, better allowing humans to predict the algorithm's behavior and plan accordingly [4,[31][32][33][34].…”

Section: Background and Motivationmentioning

confidence: 99%

Game-Based Simulation and Study of Pedestrian-Automated Vehicle Interactions

et al. 2022

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We identify the need for enhanced pedestrian–vehicle simulation tools and build such a tool to explore the interaction among pedestrian “players” and virtual human- and automated-vehicles for different scenarios taking place in an urban environment. We first present contemporary research tools and then propose the design and development of a new desktop application that facilitates pedestrian-point-of-view research. We then conduct a three-step user experience experiment, in which a small number of participants answer questions before and after using the application to interact with virtual human and automated vehicles in diverse road-crossing scenarios. Behavioral results observed in virtuality, especially when motivated by consequence, tend to simulate real life sufficiently well to inform design choices. From the simulation, we observed valuable insights into human–vehicle interactions. Upon completing this preliminary testing, we iterated the tool’s design and ultimately conducted an 89-participant study of human–vehicle interactions for three scenarios taking place in a virtual environment. Our tool raised participant awareness of autonomous vehicles and their capabilities and limitations, which is an important step in overcoming public distrust of AVs. We additionally saw that participants trust humans and technology less as drivers than in other contexts, and that pedestrians feel safer around vehicles with autonomy indicators. Further, we note that study participants increasingly feel safe with automated vehicles with increased exposure. These preliminary results, as well as the efficacy of the tool’s design, may inform future socio-technical design for automated vehicles and their human interactions.

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Section: Background and Motivationmentioning

confidence: 99%

Game-Based Simulation and Study of Pedestrian-Automated Vehicle Interactions

et al. 2022

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“…In this case, the communication relationship goes beyond the simple one-to-one encounters and includes one-to-many, many-to-one, and many-to-many interactions . Although scalability research is still in its early stages , potential scaling limitations of eHMIs, including low communication resolution and information overload, have been noted in several research articles (Robert Jr, 2019;Dey et al, 2020aDey et al, , 2021.…”

Section: Scalabilitymentioning

confidence: 99%

Designing Wearable Augmented Reality Concepts to Support Scalability in Autonomous Vehicle-Pedestrian Interaction

Tran

Parker²,

Wang³

et al. 2022

Front. Comput. Sci.

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Wearable augmented reality (AR) offers new ways for supporting the interaction between autonomous vehicles (AVs) and pedestrians due to its ability to integrate timely and contextually relevant data into the user's field of view. This article presents novel wearable AR concepts that assist crossing pedestrians in multi-vehicle scenarios where several AVs frequent the road from both directions. Three concepts with different communication approaches for signaling responses from multiple AVs to a crossing request, as well as a conventional pedestrian push button, were simulated and tested within a virtual reality environment. The results showed that wearable AR is a promising way to reduce crossing pedestrians' cognitive load when the design offers both individual AV responses and a clear signal to cross. The willingness of pedestrians to adopt a wearable AR solution, however, is subject to different factors, including costs, data privacy, technical defects, liability risks, maintenance duties, and form factors. We further found that all participants favored sending a crossing request to AVs rather than waiting for the vehicles to detect their intentions—pointing to an important gap and opportunity in the current AV-pedestrian interaction literature.

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“…A significant challenge for automated vehicles (AVs) is safe interaction with pedestrians, especially at uncontrolled mid-block crosswalks [1], [2]. Thus, it is critical that AVs can reliably predict pedestrian trajectories for safe motion planning [3].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on pedestrians intending to cross. Generally around crosswalks, pedestrians can be doing one of three actions: (1) approach crosswalk, (2) wait, or (3) cross [7]. Additionally, they can (4) walk away from crosswalk.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

Jayaraman

Tilbury

Yang

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians' trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians' gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further, we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.

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The future of pedestrian-automated vehicle interactions

Cited by 12 publications

References 7 publications

Game-Based Simulation and Study of Pedestrian-Automated Vehicle Interactions

Game-Based Simulation and Study of Pedestrian-Automated Vehicle Interactions

Designing Wearable Augmented Reality Concepts to Support Scalability in Autonomous Vehicle-Pedestrian Interaction

Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

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